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Latest Computer Science Research Topics for 2024

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Everybody sees a dream—aspiring to become a doctor, astronaut, or anything that fits your imagination. If you were someone who had a keen interest in looking for answers and knowing the “why” behind things, you might be a good fit for research. Further, if this interest revolved around computers and tech, you would be an excellent computer researcher!

As a tech enthusiast, you must know how technology is making our life easy and comfortable. With a single click, Google can get you answers to your silliest query or let you know the best restaurants around you. Do you know what generates that answer? Want to learn about the science going on behind these gadgets and the internet?

For this, you will have to do a bit of research. Here we will learn about top computer science thesis topics and computer science thesis ideas.

Top 12 Computer Science Research Topics for 2024 

Before starting with the research, knowing the trendy research paper ideas for computer science exploration is important. It is not so easy to get your hands on the best research topics for computer science; spend some time and read about the following mind-boggling ideas before selecting one.

1. Integrated Blockchain and Edge Computing Systems: A Survey, Some Research Issues, and Challenges

Welcome to the era of seamless connectivity and unparalleled efficiency! Blockchain and edge computing are two cutting-edge technologies that have the potential to revolutionize numerous sectors. Blockchain is a distributed ledger technology that is decentralized and offers a safe and transparent method of storing and transferring data.

As a young researcher, you can pave the way for a more secure, efficient, and scalable architecture that integrates blockchain and edge computing systems. So, let's roll up our sleeves and get ready to push the boundaries of technology with this exciting innovation!

Blockchain helps to reduce latency and boost speed. Edge computing, on the other hand, entails processing data close to the generation source, such as sensors and IoT devices. Integrating edge computing with blockchain technologies can help to achieve safer, more effective, and scalable architecture.

Moreover, this research title for computer science might open doors of opportunities for you in the financial sector.

2. A Survey on Edge Computing Systems and Tools

With the rise in population, the data is multiplying by manifolds each day. It's high time we find efficient technology to store it. However, more research is required for the same.

Say hello to the future of computing with edge computing! The edge computing system can store vast amounts of data to retrieve in the future. It also provides fast access to information in need. It maintains computing resources from the cloud and data centers while processing.

Edge computing systems bring processing power closer to the data source, resulting in faster and more efficient computing. But what tools are available to help us harness the power of edge computing?

As a part of this research, you will look at the newest edge computing tools and technologies to see how they can improve your computing experience. Here are some of the tools you might get familiar with upon completion of this research:

• Apache NiFi:  A framework for data processing that enables users to gather, transform, and transfer data from edge devices to cloud computing infrastructure.
• Microsoft Azure IoT Edge: A platform in the cloud that enables the creation and deployment of cutting-edge intelligent applications.
• OpenFog Consortium:  An organization that supports the advancement of fog computing technologies and architectures is the OpenFog Consortium.

3. Machine Learning: Algorithms, Real-world Applications, and Research Directions

Machine learning is the superset of Artificial Intelligence; a ground-breaking technology used to train machines to mimic human action and work. ML is used in everything from virtual assistants to self-driving cars and is revolutionizing the way we interact with computers. But what is machine learning exactly, and what are some of its practical uses and future research directions?

To find answers to such questions, it can be a wonderful choice to pick from the pool of various computer science dissertation ideas.

You will discover how computers learn several actions without explicit programming and see how they perform beyond their current capabilities. However, to understand better, having some basic programming knowledge always helps. KnowledgeHut’s Programming course for beginners will help you learn the most in-demand programming languages and technologies with hands-on projects.

During the research, you will work on and study

• Algorithm: Machine learning includes many algorithms, from decision trees to neural networks.
• Applications in the Real-world: You can see the usage of ML in many places. It can early detect and diagnose diseases like cancer. It can detect fraud when you are making payments. You can also use it for personalized advertising.
• Research Trend:  The most recent developments in machine learning research, include explainable AI, reinforcement learning, and federated learning.

While a single research paper is not enough to bring the light on an entire domain as vast as machine learning; it can help you witness how applicable it is in numerous fields, like engineering, data science & analysis, business intelligence, and many more.

Whether you are a data scientist with years of experience or a curious tech enthusiast, machine learning is an intriguing and vital field that's influencing the direction of technology. So why not dig deeper?

4. Evolutionary Algorithms and their Applications to Engineering Problems

Imagine a system that can solve most of your complex queries. Are you interested to know how these systems work? It is because of some algorithms. But what are they, and how do they work? Evolutionary algorithms use genetic operators like mutation and crossover to build new generations of solutions rather than starting from scratch.

This research topic can be a choice of interest for someone who wants to learn more about algorithms and their vitality in engineering.

Evolutionary algorithms are transforming the way we approach engineering challenges by allowing us to explore enormous solution areas and optimize complex systems.

The possibilities are infinite as long as this technology is developed further. Get ready to explore the fascinating world of evolutionary algorithms and their applications in addressing engineering issues.

5. The Role of Big Data Analytics in the Industrial Internet of Things

Datasets can have answers to most of your questions. With good research and approach, analyzing this data can bring magical results. Welcome to the world of data-driven insights! Big Data Analytics is the transformative process of extracting valuable knowledge and patterns from vast and complex datasets, boosting innovation and informed decision-making.

This field allows you to transform the enormous amounts of data produced by IoT devices into insightful knowledge that has the potential to change how large-scale industries work. It's like having a crystal ball that can foretell.

Big data analytics is being utilized to address some of the most critical issues, from supply chain optimization to predictive maintenance. Using it, you can find patterns, spot abnormalities, and make data-driven decisions that increase effectiveness and lower costs for several industrial operations by analyzing data from sensors and other IoT devices.

The area is so vast that you'll need proper research to use and interpret all this information. Choose this as your computer research topic to discover big data analytics' most compelling applications and benefits. You will see that a significant portion of industrial IoT technology demands the study of interconnected systems, and there's nothing more suitable than extensive data analysis.

6. An Efficient Lightweight Integrated Blockchain (ELIB) Model for IoT Security and Privacy

Are you concerned about the security and privacy of your Internet of Things (IoT) devices? As more and more devices become connected, it is more important than ever to protect the security and privacy of data. If you are interested in cyber security and want to find new ways of strengthening it, this is the field for you.

ELIB is a cutting-edge solution that offers private and secure communication between IoT devices by fusing the strength of blockchain with lightweight cryptography. This architecture stores encrypted data on a distributed ledger so only parties with permission can access it.

But why is ELIB so practical and portable? ELIB uses lightweight cryptography to provide quick and effective communication between devices, unlike conventional blockchain models that need complicated and resource-intensive computations.

Due to its increasing vitality, it is gaining popularity as a research topic as someone aware that this framework works and helps reinstate data security is highly demanded in financial and banking.

7. Natural Language Processing Techniques to Reveal Human-Computer Interaction for Development Research Topics

Welcome to the world where machines decode the beauty of the human language. With natural language processing (NLP) techniques, we can analyze the interactions between humans and computers to reveal valuable insights for development research topics. It is also one of the most crucial PhD topics in computer science as NLP-based applications are gaining more and more traction.

Etymologically, natural language processing (NLP) is a potential technique that enables us to examine and comprehend natural language data, such as discussions between people and machines. Insights on user behaviour, preferences, and pain areas can be gleaned from these encounters utilizing NLP approaches.

But which specific areas should we leverage on using NLP methods? This is precisely what you’ll discover while doing this computer science research.

Gear up to learn more about the fascinating field of NLP and how it can change how we design and interact with technology, whether you are a UX designer, a data scientist, or just a curious tech lover and linguist.

8. All One Needs to Know About Fog Computing and Related Edge Computing Paradigms: A Complete Survey

If you are an IoT expert or a keen lover of the Internet of Things, you should leap and move forward to discovering Fog Computing. With the rise of connected devices and the Internet of Things (IoT), traditional cloud computing models are no longer enough. That's where fog computing and related edge computing paradigms come in.

Fog computing is a distributed approach that brings processing and data storage closer to the devices that generate and consume data by extending cloud computing to the network's edge.

As computing technologies are significantly used today, the area has become a hub for researchers to delve deeper into the underlying concepts and devise more and more fog computing frameworks. You can also contribute to and master this architecture by opting for this stand-out topic for your research.

9. Artificial Intelligence (AI)

The field of artificial intelligence studies how to build machines with human-like cognitive abilities and it is one of the  trending research topics in computer science . Unlike humans, AI technology can handle massive amounts of data in many ways. Some important areas of AI where more research is needed include:  

• Deep learning: Within the field of Machine Learning, Deep Learning mimics the inner workings of the human brain to process and apply judgements based on input.   
• Reinforcement learning:  With artificial intelligence, a machine can learn things in a manner akin to human learning through a process called reinforcement learning.  
• Natural Language processing (NLP):  While it is evident that humans are capable of vocal communication, machines are also capable of doing so now! This is referred to as "natural language processing," in which computers interpret and analyse spoken words.  

10. Digital Image Processing

Digital image processing is the process of processing digital images using computer algorithms.  Recent research topics in computer science  around digital image processing are grounded in these techniques. Digital image processing, a subset of digital signal processing, is superior to analogue image processing and has numerous advantages. It allows several algorithms to be applied to the input data and avoids issues like noise accumulation and signal distortion during processing. Digital image processing comes in a variety of forms for research. The most recent thesis and research topics in digital image processing are listed below:  

• Image Acquisition  
• Image Enhancement  
• Image Restoration  
• Color Image Processing  
• Wavelets and Multi Resolution Processing  
• Compression  
• Morphological Processing  

11. Data Mining

The method by which valuable information is taken out of the raw data is called data mining. Using various data mining tools and techniques, data mining is used to complete many tasks, including association rule development, prediction analysis, and clustering. The most effective method for extracting valuable information from unprocessed data in data mining technologies is clustering. The clustering process allows for the analysis of relevant information from a dataset by grouping similar and dissimilar types of data. Data mining offers a wide range of trending  computer science research topics for undergraduates :  

• Data Spectroscopic Clustering  
• Asymmetric spectral clustering  
• Model-based Text Clustering  
• Parallel Spectral Clustering in Distributed System  
• Self-Tuning Spectral Clustering  

12. Robotics

We explore how robots interact with their environments, surrounding objects, other robots, and humans they are assisting through the research, design, and construction of a wide range of robot systems in the field of robotics. Numerous academic fields, including mathematics, physics, biology, and computer science, are used in robotics. Artificial intelligence (AI), physics simulation, and advanced sensor processing (such as computer vision) are some of the key technologies from computer science.  Msc computer science project topic s focus on below mentioned areas around Robotics:  

• Human Robot collaboration  
• Swarm Robotics  
• Robot learning and adaptation  
• Soft Robotics  
• Ethical considerations in Robotics  

How to Choose the Right Computer Science Research Topics?  

Choosing the  research areas in computer science  could be overwhelming. You can follow the below mentioned tips in your pursuit:  

• Chase Your Curiosity:  Think about what in the tech world keeps you up at night, in a good way. If it makes you go "hmm," that's the stuff to dive into.  
• Tech Trouble Hunt: Hunt for the tech troubles that bug you. You know, those things that make you mutter, "There's gotta be a better way!" That's your golden research nugget.  
• Interact with Nerds: Grab a coffee (or your beverage of choice) and have a laid-back chat with the tech geeks around you. They might spill the beans on cool problems or untapped areas in computer science.  
• Resource Reality Check: Before diving in, do a quick reality check. Make sure your chosen topic isn't a resource-hungry beast. You want something you can tackle without summoning a tech army.  
• Tech Time Travel: Imagine you have a time machine. What future tech would blow your mind? Research that takes you on a journey to the future is like a time travel adventure.  
• Dream Big, Start Small:  Your topic doesn't have to change the world on day one. Dream big, but start small. The best research often grows from tiny, curious seeds.  
• Be the Tech Rebel: Don't be afraid to be a bit rebellious. If everyone's zigging, you might want to zag. The most exciting discoveries often happen off the beaten path.  
• Make it Fun: Lastly, make sure it's fun. If you're going to spend time on it, might as well enjoy the ride. Fun research is the best research.  

Tips and Tricks to Write Computer Science Research Topics

Before starting to explore these hot research topics in computer science you may have to know about some tips and tricks that can easily help you.

• Know your interest.
• Choose the topic wisely.
• Make proper research about the demand of the topic.
• Get proper references.
• Discuss with experts.

By following these tips and tricks, you can write a compelling and impactful computer research topic that contributes to the field's advancement and addresses important research gaps.

Why is Research in Computer Science Important?

Computers and technology are becoming an integral part of our lives. We are dependent on them for most of our work. With the changing lifestyle and needs of the people, continuous research in this sector is required to ease human work. However, you need to be a certified researcher to contribute to the field of computers. You can check out Advance Computer Programming certification to learn and advance in the versatile language and get hands-on experience with all the topics of C# application development.

1. Innovation in Technology

Research in computer science contributes to technological advancement and innovations. We end up discovering new things and introducing them to the world. Through research, scientists and engineers can create new hardware, software, and algorithms that improve the functionality, performance, and usability of computers and other digital devices.

2. Problem-Solving Capabilities

From disease outbreaks to climate change, solving complex problems requires the use of advanced computer models and algorithms. Computer science research enables scholars to create methods and tools that can help in resolving these challenging issues in a blink of an eye.

3. Enhancing Human Life

Computer science research has the potential to significantly enhance human life in a variety of ways. For instance, researchers can produce educational software that enhances student learning or new healthcare technology that improves clinical results. If you wish to do Ph.D., these can become interesting computer science research topics for a PhD.

4. Security Assurance

As more sensitive data is being transmitted and kept online, security is our main concern. Computer science research is crucial for creating new security systems and tactics that defend against online threats.

From machine learning and artificial intelligence to blockchain, edge computing, and big data analytics, numerous trending computer research topics exist to explore. One of the most important trends is using cutting-edge technology to address current issues. For instance, new IoT security and privacy opportunities are emerging by integrating blockchain and edge computing. Similarly, the application of natural language processing methods is assisting in revealing human-computer interaction and guiding the creation of new technologies.

Another trend is the growing emphasis on sustainability and moral considerations in technological development. Researchers are looking into how computer science might help in innovation.

With the latest developments and leveraging cutting-edge tools and techniques, researchers can make meaningful contributions to the field and help shape the future of technology. Going for Full-stack Developer online training will help you master the latest tools and technologies. 

Frequently Asked Questions (FAQs)

Research in computer science is mainly focused on different niches. It can be theoretical or technical as well. It completely depends upon the candidate and his focused area. They may do research for inventing new algorithms or many more to get advanced responses in that field.  

Yes, moreover it would be a very good opportunity for the candidate. Because computer science students may have a piece of knowledge about the topic previously. They may find Easy thesis topics for computer science to fulfill their research through KnowledgeHut. 

There are several scopes available for computer science. A candidate can choose different subjects such as AI, database management, software design, graphics, and many more. 

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic. 

Overview: CompSci Research Topics

• Algorithms & data structures
• Artificial intelligence ( AI )
• Computer networking
• Database systems
• Human-computer interaction
• Information security (IS)
• Software engineering
• Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

• An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
• A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
• An evaluation of machine learning algorithms used for recommender systems in streaming services
• A review of approximation algorithm approaches for solving NP-hard problems
• An analysis of parallel algorithms for high-performance computing of genomic data
• The influence of data structures on optimal algorithm design and performance in Fintech
• A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
• A comparison of streaming algorithm performance for the detection of elephant flows
• A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
• Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
• Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
• A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
• A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
• Exploring the feasibility of applying information theory to feature extraction in retail datasets.
• Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

• Applying deep learning algorithms for speech recognition in speech-impaired children
• A review of the impact of artificial intelligence on decision-making processes in stock valuation
• An evaluation of reinforcement learning algorithms used in the production of video games
• An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
• An analysis of the ethical and social implications of artificial intelligence-based automated marking
• The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
• An examination of the use of artificial intelligence in orthopaedic surgery
• The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
• An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
• A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

• An analysis of the impact of 5G technology on internet penetration in rural Tanzania
• Assessing the role of software-defined networking (SDN) in modern cloud-based computing
• A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
• Exploring the influence of cloud computing on security risks in fintech.
• An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
• Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
• An evaluation of the challenges and opportunities in 6G wireless network adoption
• The role of network congestion control algorithms in improving network performance on streaming platforms
• An analysis of network coding-based approaches for data security
• Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Topics & Ideas: Database Systems

• An analysis of big data management systems and technologies used in B2B marketing
• The impact of NoSQL databases on data management and analysis in smart cities
• An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
• Exploring the role of data warehousing and business intelligence in global consultancies
• An analysis of the use of graph databases for data modelling and analysis in recommendation systems
• The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
• An examination of the challenges and opportunities of distributed databases in supply chain management
• Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
• An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
• Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

• An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
• An exploration of how artificial intelligence is changing human-computer interaction patterns in children
• An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
• Assessing the influence of virtual and augmented reality on consumer purchasing patterns
• An examination of the use of gesture-based interfaces in architecture
• Exploring the impact of ease of use in wearable technology on geriatric user
• Evaluating the ramifications of gamification in the Metaverse
• A systematic review of user experience (UX) design advances associated with Augmented Reality
• A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
• Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Topics & Ideas: Information Security

• A bibliometric review of current trends in cryptography for secure communication
• An analysis of secure multi-party computation protocols and their applications in cloud-based computing
• An investigation of the security of blockchain technology in patient health record tracking
• A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
• A systematic review of secure data storage solutions used for cloud computing in the fintech industry
• An analysis of intrusion detection and prevention systems used in the healthcare sector
• Assessing security best practices for IoT devices in political offices
• An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
• A comparative study of digital signature schemes adoption in property transfers
• An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

• A study of agile software development methodologies and their impact on project success in pharmacology
• Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
• A study of the impact of DevOps practices on software development and delivery in the healthcare sector
• An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
• A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
• An investigation of software testing techniques and methodologies for subscription-based offerings
• A review of software security practices and techniques for protecting against phishing attacks from social media
• An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
• Exploring the impact of software development outsourcing on project success in multinational contexts
• An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• An array-based optimization framework for query processing and data analytics (Chen, 2021)
• Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
• Embedding constructural documentation in unit tests (Nassif, 2019)
• PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
• Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
• Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
• Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
• Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
• Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
• Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
• Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
• A state-based approach to context modeling and computing (Yue, 2019)
• A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
• HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments is in my favour. May i get the proper material about that ?

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Over the past years we've increased our strength in areas such as artificial intelligence, computer graphics, systems, security, machine learning, and digital libraries, while maintaining our depth in traditional areas such as theory, programming languages and scientific computing.  You can find out more about our research here . 

The department provides an exceptionally open and friendly atmosphere that encourages the sharing of ideas across all areas. 

Cornell is located in the heart of the Finger Lakes region. This beautiful area provides many opportunities for recreational activities such as sailing, windsurfing, canoeing, kayaking, both downhill and cross-country skiing, ice skating, rock climbing, hiking, camping, and brewery/cider/wine-tasting. In fact, Cornell offers courses in all of these activities.

The Cornell Tech campus in New York City is located on Roosevelt Island.  Cornell Tech  is a graduate school conceived and implemented expressly to integrate the study of technology with business, law, and design. There are now over a half-dozen masters programs on offer as well as doctoral studies.

FAQ with more information about the two campuses .

Ph.D. Program Structure

Each year, about 30-40 new Ph.D. students join the department. During the first two semesters, students become familiar with the faculty members and their areas of research by taking graduate courses, attending research seminars, and participating in research projects. By the end of the first year, each student selects a specific area and forms a committee based on the student's research interests. This “Special Committee” of three or more faculty members will guide the student through to a Ph.D. dissertation. Ph.D. students that decide to work with a faculty member based at Cornell Tech typically move to New York City after a year in Ithaca.

The Field believes that certain areas are so fundamental to Computer Science that all students should be competent in them. Ph.D. candidates are expected to demonstrate competency in four areas of computer science at the high undergraduate level: theory, programming languages, systems, and artificial intelligence.

Each student then focuses on a specific topic of research and begins a preliminary investigation of that topic. The initial results are presented during a comprehensive oral evaluation, which is administered by the members of the student's Special Committee. The objective of this examination, usually taken in the third year, is to evaluate a student's ability to undertake original research at the Ph.D. level.

The final oral examination, a public defense of the dissertation, is taken before the Special Committee.

To encourage students to explore areas other than Computer Science, the department requires that students complete an outside minor. Cornell offers almost 90 fields from which a minor can be chosen. Some students elect to minor in related fields such as Applied Mathematics, Information Science, Electrical Engineering, or Operations Research. Others use this opportunity to pursue interests as diverse as Music, Theater, Psychology, Women's Studies, Philosophy, and Finance.

The computer science Ph.D. program complies with the requirements of the Cornell Graduate School , which include requirements on residency, minimum grades, examinations, and dissertation.

The Department also administers a very small 2-year Master of Science program (with thesis). Students in this program serve as teaching assistants and receive full tuition plus a stipend for their services.

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Academics   /   Graduate PhD in Computer Science

The doctor of philosophy in computer science program at Northwestern University primarily prepares students to become expert independent researchers. PhD students conduct original transformational research in extant and emerging computer science topics. Students work alongside top researchers to advance the core CS fields from Theory to AI and Systems and Networking . In addition, PhD students have the opportunity to collaborate with CS+X faculty who are jointly appointed between CS and disciplines including business, law, economics, journalism, and medicine.

Joining a Track

Doctor of philosophy in computer science students follow the course requirements, qualifying exam structure, and thesis process specific to one of five tracks :

• Artificial Intelligence and Machine Learning
• Computer Engineering

Within each track, students explore many areas of interest, including programming languages , security and privacy and human-computer interaction .

Learn more about computer science research areas

Curriculum and Requirements

The focus of the CS PhD program is learning how to do research by doing research, and students are expected to spend at least 50% of their time on research. Students complete ten graduate curriculum requirements (including COMP_SCI 496: Introduction to Graduate Studies in Computer Science ), and additional course selection is tailored based on individual experience, research track, and interests. Students must also successfully complete a qualifying exam to be admitted to candidacy.

CS PhD Manual Apply now

Request More Information

Download a PDF program guide about your program of interest and get in contact with our graduate admissions staff.

Request info about the PhD degree

Opportunities for PhD Students

Cognitive science certificate.

Computer science PhD students may earn a specialization in cognitive science by taking six cognitive science courses. In addition to broadening a student’s area of study and improving their resume, students attend cognitive science events and lectures, they can receive conference travel support, and they are exposed to cross-disciplinary exchanges.

The Crown Family Graduate Internship Program

PhD candidates may elect to participate in the Crown Family Graduate Internship Program. This opportunity allows the doctoral candidate to gain practical experience in industry or in national research laboratories in areas closely related to their research.

Management for Scientists and Engineers Certificate Program

The certificate program — jointly offered by The Graduate School and Kellogg School of Management — provides post-candidacy doctoral students with a basic understanding of strategy, finance, risk and uncertainty, marketing, accounting and leadership. Students are introduced to business concepts and specific frameworks for effective management relevant to both for-profit and nonprofit sectors.

Career Paths

Recent graduates of the computer science PhD program are pursuing careers in industry & research labs, academia, and startups.

• Georgia Institute of Technology
• Illinois Institute of Technology
• Northeastern
• University of Pittsburgh
• University of Rochester
• University of Washington
• Naval Research Laboratory
• Northwestern University

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• Adobe Research
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• Oak Ridge National Laboratory

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What Students Are Saying

"One great benefit of Northwestern is the collaborative effort of the CS department that enabled me to work on projects involving multiple faculty, each with their own diverse set of expertise.

Northwestern maintains a great balance: you will work on leading research at a top-tier institution, and you won't get lost in the mix."

— Brian Suchy, PhD Candidate, Computer Systems

What Alumni Are Saying

"In the early stage of my PhD program, I took several courses from the Department of Economics and the Kellogg School of Management and, later, I started collaborating with researchers in those areas. The experience taught me how to have an open mind to embrace and work with people with different backgrounds."

— Yiding Feng (PhD '21), postdoctoral researcher, Microsoft Research Lab – New England

Read an alumni profile of Yiding Feng

"My work at IBM Research involves bringing together symbolic and deep learning techniques to solve problems in interpretable, effective ways, which means I must draw upon the research I did at Northwestern quite frequently."

— Maxwell Crouse (PhD '21), AI Research Scientist, IBM Research

Read an alumni profile of Maxwell Crouse

The theory group here is very warm and close-knit. Starting a PhD is daunting, and it is comforting to have a community I can lean on.

— Vaidehi Srinivas, PhD Candidate, CS Theory

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Phd program, find your passion for research.

Duke Computer Science gives incoming students an opportunity to investigate a range of topics, research problems, and research groups before committing to an advisor in the first year. Funding from the department and Duke makes it possible to attend group meetings, seminars, classes and colloquia. Students may work on multiple problems simultaneously while finding the topic that will motivate them through their first project. Sharing this time of learning and investigation with others in the cohort helps create lasting collaborators and friends.

Write a research proposal the first year and finish the research the second under the supervision of the chosen advisor and committee; present the research results to the committee and peers. Many students turn their RIP work into a conference paper and travel to present it.

Course work requirements are written to support the department's research philosophy. Pass up to four of the required six courses in the first two years to give time and space for immersing oneself in the chosen area.

Years three through five continue as the students go deeper and deeper into a research area and their intellectual community broadens to include collaborators from around the world. Starting in year three, the advisor funds the student's work, usually through research grants. The Preliminary exam that year is the opportunity for the student to present their research to date, to share work done by others on the topic, and to get feedback and direction for the Ph.D. from the committee, other faculty, and peers.

Most Ph.D students defend in years five and six. While Duke and the department guarantee funding through the fifth year, advisors and the department work with students to continue support for work that takes longer.

Teaching is a vital part of the Ph.D. experience. Students are required to TA for two semesters, although faculty are ready to work with students who want more involvement. The Graduate School's Certificate in College Teaching offers coursework, peer review, and evaluation of a teaching portfolio for those who want to teach. In addition, the Department awards a Certificates of Distinction in Teaching for graduating PhD students who have demonstrated excellence in and commitment to teaching and mentoring.

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Computer Science, PhD

Computer science phd degree.

In the Computer Science program, you will learn both the fundamentals of computation and computation’s interaction with the world. Your work will involve a wide range of areas including theoretical computer science, artificial intelligence and machine learning, economics and computer science, privacy and security, data-management systems, intelligent interfaces, operating systems, computer graphics, computational linguistics, robotics, networks, architectures, program languages, and visualization.

You will be involved with researchers in several interdisciplinary initiatives across the University, such as the Center for Research on Computation and Society , the Data Science Initiative , and the Berkman Klein Center for Internet and Society .

Examples of projects current and past students have worked on include leveraging machine learning to solve real-world sequential decision-making problems and using artificial intelligence to help conservation and anti-poaching efforts around the world.

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Computer Science Degree

Harvard School of Engineering offers a  Doctor of Philosophy (Ph.D) degree in Computer Science , conferred through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences. Prospective students apply through Harvard Griffin GSAS; in the online application, select “Engineering and Applied Sciences” as your program choice and select "PhD Computer Science" in the Area of Study menu.

In addition to the Ph.D. in Computer Science, the Harvard School of Engineering also offers master’s degrees in  Computational Science and Engineering as well as in Data Science which may be of interest to applicants who wish to apply directly to a master’s program.

Computer Science Career Paths

Graduates of the program have gone on to a range of careers in industry in companies like Riot Games as game director and Lead Scientist at Raytheon. Others have positions in academia at University of Pittsburgh, Columbia, and Stony Brook. More generally, common career paths for individuals with a PhD in computer science include: academic researcher/professor, industry leadership roles, industry research scientist, data scientist, entrepreneur/startup founder, product developer, and more.

Admissions & Academic Requirements

Prospective students apply through the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS). In the online application, select  “Engineering and Applied Sciences” as your program choice and select "PhD Engineering Sciences: Electrical Engineering​." Please review the  admissions requirements and other information  before applying. Our website also provides  admissions guidance ,  program-specific requirements , and a  PhD program academic timeline . In the application for admission, select “Engineering and Applied Sciences” as your degree program choice and your degree and area of interest from the “Area of Study“ drop-down. PhD applicants must complete the Supplemental SEAS Application Form as part of the online application process.

Academic Background

Applicants typically have bachelor’s degrees in the natural sciences, mathematics, computer science, or engineering.

Standardized Tests

GRE General: Not Accepted

Computer Science Faculty & Research Areas

View a list of our computer science faculty  and  computer science affiliated research areas . Please note that faculty members listed as “Affiliates" or "Lecturers" cannot serve as the primary research advisor.

Computer Science Centers & Initiatives

View a list of the research centers & initiatives  at SEAS and the computer science faculty engagement with these entities .

Graduate Student Clubs

Graduate student clubs and organizations bring students together to share topics of mutual interest. These clubs often serve as an important adjunct to course work by sponsoring social events and lectures. Graduate student clubs are supported by the Harvard Kenneth C. Griffin School of Arts and Sciences. Explore the list of active clubs and organizations .

Funding and Scholarship

Learn more about financial support for PhD students.

• How to Apply

Learn more about how to apply  or review frequently asked questions for prospective graduate students.

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Computer Science, PhD

Researchers from all fields use computational models to analyze massive amounts of data. There’s a growing need for computer scientists who can collaborate with other domains and also research ways to improve the networks, the operating systems, and the multitude of devices that are integrated into our daily lives.

Our PhD in Computer Science program prepares you for a career in research and/or teaching by providing the necessary course work and collaborative environment for both supervised and independent research. Our PhD students are researching mobile apps to help improve the science of learning, building operating systems for high-performance computers, addressing security and privacy from a data-oriented perspective, improving computer performance, and more.

You’ll have the opportunity to take part in the diverse faculty research collaborations with other departments and programs within the university, such as the Learning Research and Development Center, the School of Engineering, and the School of Medicine.

Degree Requirements

Course requirements.

The PhD degree requires 72 credits of formal course work, independent study, directed study, and/or dissertation research. In addition to the credit requirement, twelve courses are required for the PhD categorized as follows: four foundation courses, six elective courses,  CS 2001  (Research Topics in Computer Science) and  CS 2002  (Research Experiences in Computer Science). CS 2001 must be taken during the first fall term and CS 2002 must be taken during the following spring term.

The four foundation courses must cover each of the following four foundation areas.

Architecture and Compilers

• CS 2410 - Computer Architecture OR
• CS 2210 - Compiler Design

Operating Systems and Networks

• ​ CS 2510 - Computer Operating Systems OR
• CS 2520 - Wide Area Networks

Artifical Intelligence and Database Systems

• CS 2710 - Foundations of Artificial Intelligence OR
• CS 2550 - Principles of Database Systems

Theory and Algorithms

• CS 2110 - Introduction to Theory of Computation OR
• CS 2150 - Design and Analysis of Algorithms

The six elective courses must be 2100-level or higher CSD courses and cannot be independent study courses ( CS 2990 ,  CS 3000 ), graduate internship ( CS 2900 ), co-ops (CS 2905), thesis project or research courses ( CS 2910 ,  CS 3900 ). At least two of the six courses must be at the 3000-level.

The following requirements apply to the 12 required courses:

• All must be taken for a letter grade.
• Students are required to complete the four required foundation area courses by the end of the fourth regular term of study. Regular terms include the fall and spring and do not include the summer session.
• The student must receive a grade of B or better in each of the required foundation area courses, and a grade of B-or better in each of the six additional courses; in addition, he or she must maintain an overall average QPA of 3.0 or better.
• No more than 6 of the 12 courses may be taken outside of the CSD. This includes up to four courses that are transfered from other universities at the time of admission. All courses from outside the CSD must be approved by GPEC.
• All 12 courses must be successfully completed before admission to candidacy for the PhD (This normally occurs when the student passes the oral examination during the dissertation proposal.)

For full degree requirements details, visit the Computer Science course catalog .

Admissions Requirements

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PhD in Computer Science

The PhD in Computer Science is a small and selective program at Pace University that aims to cultivate advanced computing research scholars and professionals who will excel in both industry and academia. By enrolling in this program, you will be on your way to joining a select group at the very nexus of technological thought and application.

Learn more about the PhD in Computer Science .

Forms and Research Areas

General forms.

• PhD Policies and Procedures Manual – The manual contains all the information you need before, during, and toward the end of your studies in the PhD program.
• Advisor Approval Form (PDF) – Completed by student and approved by faculty member agreeing to the role as advisor.
• Committee Member Approval Form (PDF) – Completed by student with signatures of each faculty member agreeing to be on dissertation committee.
• Change in Advisor or Committee Member Approval Form (PDF) – Completed by student with the approval of new advisor or committee member. Department Chair approval needed.
• Qualifying Exam Approval Form (PDF) – Complete and return form to the Program Coordinator no later than Week 6 of the semester.

Dissertation Proposal of Defense Forms

• Application for the Dissertation Proposal of Defense Form (PDF) – Completed by student with the approval of committee members that dissertation proposal is sufficient to defend. Completed form and abstract and submitted to program coordinator for scheduling of defense.
• Dissertation Proposal Defense Evaluation Form (PDF) – To be completed by committee members after student has defended his dissertation proposal.

Final Dissertation Defense Forms

• Dissertation Pre- Defense Approval Form (PDF) – Committee approval certifying that the dissertation is sufficiently developed for a defense.
• Dissertation Defense Evaluation Form (PDF) – Completed by committee members after student has defended his dissertation.

All completed forms submitted to the program coordinator.

Research Areas

The Seidenberg School’s PhD in Computer Science covers a wealth of research areas. We pride ourselves on engaging with every opportunity the computer science field presents. Check out some of our specialties below for examples of just some of the topics we cover at Seidenberg. If you have a particular field of study you are interested in that is not listed below, just get in touch with us and we can discuss opportunities and prospects.

Some of the research areas you can explore at Seidenberg include:

Algorithms And Distributed Computing

Algorithms research in Distributed Computing contributes to a myriad of applications, such as Cloud Computing, Grid Computing, Distributed Databases, Cellular Networks, Wireless Networks, Wearable Monitoring Systems, and many others. Being traditionally a topic of theoretical interest, with the advent of new technologies and the accumulation of massive volumes of data to analyze, theoretical and experimental research on efficient algorithms has become of paramount importance. Accordingly, many forefront technology companies base 80-90% of their software-developer hiring processes on foundational algorithms questions. The Seidenberg faculty has internationally recognized strength in algorithms research for Ad-hoc Wireless Networks embedded in IoT Systems, Mobile Networks, Sensor Networks, Crowd Computing, Cloud Computing, and other related areas. Collaborations on these topics include prestigious research institutions world-wide.

Machine Learning In Medical Image Analysis

Machine learning in medical imaging is a potentially disruptive technology. Deep learning, especially convolutional neural networks (CNN), have been successfully applied in many aspects of medical image analysis, including disease severity classification, region of interest detection, segmentation, registration, disease progression prediction, and other tasks. The Seidenberg School maintains a research track on applying cutting-edge machine learning methods to assist medical image analysis and clinical data fusion. The purpose is to develop computer-aided and decision-supporting systems for medical research and applications.

Pattern recognition, artificial intelligence, data mining, intelligent agents, computer vision, and data mining are topics that are all incorporated into the field of robotics. The Seidenberg School has a robust robotics program that combines these topics in a meaningful program which provides students with a solid foundation in the robotics sphere and allows for specialization into deeper research areas.

Cybersecurity

The Seidenberg School has an excellent track record when it comes to cybersecurity research. We lead the nation in web security, developing secure web applications, and research into cloud security and trust. Since 2004, Seidenberg has been designated a Center of Academic Excellence in Information Assurance Education three times by the National Security Agency and the Department of Homeland Security and is now a Center of Academic Excellence in Cyber Defense Education. We also secured more than $2,000,000 in federal and private funding for cybersecurity research during the past few years.

Pattern Recognition And Machine Learning

Just as humans take actions based on their sensory input, pattern recognition and machine learning systems operate on raw data and take actions based on the categories of the patterns. These systems can be developed from labeled training data (supervised learning) or from unlabeled training data (unsupervised learning). Pattern recognition and machine learning technology is used in diverse application areas such as optical character recognition, speech recognition, and biometrics. The Seidenberg faculty has recognized strengths in many areas of pattern recognition and machine learning, particularly handwriting recognition and pen computing, speech and medical applications, and applications that combine human and machine capabilities.

A popular application of pattern recognition and machine learning in recent years has been in the area of biometrics. Biometrics is the science and technology of measuring and statistically analyzing human physiological and behavioral characteristics. The physiological characteristics include face recognition, DNA, fingerprint, and iris recognition, while the behavioral characteristics include typing dynamics, gait, and voice. The Seidenberg faculty has nationally recognized strength in biometrics, particularly behavioral biometrics dealing with humans interacting with computers and smartphones.

Big Data Analytics

The term “Big Data” is used for data so large and complex that it becomes difficult to process using traditional structured data processing technology. Big data analytics is the science that enables organizations to analyze a mixture of structured, semi-structured, and unstructured data in search of valuable information and insights. The data come from many areas, including meteorology, genomics, environmental research, and the internet. This science uses many machine learning algorithms and the challenges include data capture, search, storage, analysis, and visualization.

Business Process Modeling

Business Process Modeling is the emerging technology for automating the execution and integration of business processes. The BPMN-based business process modeling enables precise modeling and optimization of business processes, and BPEL-based automatic business execution enables effective computing service and business integration and effective auditing. Seidenberg was among the first in the nation to introduce BPM into curricula and research.

Educational Approaches Using Emerging Computing Technologies

The traditional classroom setting doesn’t suit everyone, which is why many teachers and students are choosing to use the web to teach, study, and learn. Pace University offers online bachelor's degrees through NACTEL and Pace Online, and many classes at the Seidenberg School and Pace University as a whole are available to students online.

The Seidenberg School’s research into new educational approaches include innovative spiral education models, portable Seidenberg labs based on cloud computing and computing virtualization with which students can work in personal enterprise IT environment anytime anywhere, and creating new semantic tools for personalized cyber-learning.

Tips to Become a Better (Computer Science) Ph.D. Student

Why does the world need another blog post.

There are already a lot of great blogs posts about the computer science Ph.D. experience, each approaching it from a different angle (the whole process of a Ph.D., how to choose your research topic, etc.). However, the ideas presented in most of these blog post come from the experience of one person while this blog is a condensed summary of in-depth talks with more than five professors and three Ph.D. student during the YArch workshop at HPCA’19. During these conversations, we discussed topics that are important for early year computer science Ph.D. students . We chose ten ideas we found most impactful to us, and explain five of them in detail and present the other five as short tips.

Research > Courses

Be professional, read a lot and read broadly, impact humankind, don’t give up on your research topic easily, aim for top-tier conferences.

• Use existing resources in your groups

You are powerful!

Focus on publishing.

If you have more ideas, please comment at the bottom of this post!

Other amazing blogs out there:

• The Ph.D. Grind
• Tips: How to Do Research
• So long, and thanks for the Ph.D.!
• Graduate School Survival Guide
• Tips for a New Computer Architecture PhD Student

Young Ph.D. students tend to spend too much time on courses. However, research outweighs courses.

Take courses with a grain of salt

Courses are not as important as they seem to be. The priority of a Ph.D. student is to do research – the earlier you start your research, the better off you’ll be in the long run.

However, don’t go to extremes ! A poor grade can also be a huge problem. You should always be familiar with the requirement of qualification exams or generals and meet all the standards about the courses.

Remember the main ideas of courses

Trapping ourselves in trivial details of a course is easy. However, most of the specifics are not important to our research even if the topic is related to our area.

A good approach is to use what you’ve learned from one course and apply it to a different field (e.g., taking an analysis tool from a compiler course and applying it in computer networks).

Treat your Ph.D. as a job. You get paid (albeit not much) for being a Ph.D. candidate, so make your work worth the money. This professional mindset should also be apparent to your advisor. Some advisors take on a more hands-off approach, for instance letting you work from home, but this is no reason for slacking; you should be responsible for your research schedule, such as reminding your advisor of plans from previous group meetings. Your status is not that of a student but rather that of a peer in the research community.

Though it can be very daunting starting out, reading papers is an essential part of the Ph.D. life. Previously, you may have read papers when it was necessary for a class or a project. However, you should put reading papers in your daily routine. Doing so allows you to draw inspiration from a sea of knowledge and prevents yourself from reinventing the wheel. Besides, it’s a great way to be productive on a slow day.

Make a plan to read

When scheduling your day, assign one period just for reading papers. You can read one paper in depth or compare several papers; regardless of your choice, allotting time to this task is the key.

Read broadly

Reading papers from different subfields of computer science is a great way to learn the jargon, the method, and the mindset of researchers in each field. This can be the first step towards discovering opportunities for collaboration.

It is not uncommon for a Ph.D. student to spend several years building a system that turns out to be fundamentally flawed or not as applicable as expected. Don’t worry! There is nothing wrong with failing, and perhaps we should even expect failure to be part of the journey. But we should aim to fail early in order to have time to work on another project (and graduate!).

Perform a limit study

Perform a quick limit study before sticking with a project. A limit study includes in-depth analyses of implicit assumptions we make when coming up with an idea, a related works search, and the potential of the work if everything goes well. A great limit study can itself be a publishable paper. An example can be found here .

Hacky implementation can be useful

Being a researcher, your work is to develop proof-of-concepts. Nevertheless, you need to demonstrate that your concept is sound for the simplest of cases before continuing to the full-blown system. Hack in the minimum set to show that your idea is possible while resisting the temptation to build a robust infrastructure – if your idea fails, you will know to stop earlier.

Impacting humankind may sound too ambitious, but it should be the ultimate reason why we embark on this journey.

Choose an impactful research topic

In terms of how our Ph.D. research could impact human knowledge, I would like to refer to The Illustrated Guide to a Ph.D. by Matt Might. All we will do in five years is pushing the boundary of human knowledge by a minute margin. Choose a topic that you are able to contribute to, feel passionate about, and can explain the importance of to a layman in a 3-min talk.

Check out why Matt Might changed his research focus from programming languages to precise medicine.

How can our research actually impact people from other fields?

A survey paper by the Liberty Research Group sheds light on how the improvement of programming tools impacts ( computational scientists ) all scientists. Thinking about how your research affects people from other fields can help you define the scope of your contribution.

At some point, we will get bored with our research topic and find something else interesting. Think twice before switching topics. You must differentiate between your project heading nowhere and you getting tired of being stuck.

You should focus on publishing at only top-tier conferences. Don’t consider second-tier venues unless the work has been rejected several times by top-tier conferences. This can prevent you from doing incremental work to make your publication list look better.

Use existing resources in your group

For many fields in computer science, a mature infrastructure requires several years of development by multiple graduate students. Think about how to make use of the infrastructure and resources in the group to boost your research progress.

Even though we are just junior graduate students, we can have a massive impact on ourselves, our group, and even our department. For example, if there is no reading group for your field in your department, start one!

Needless to say, publications are essential since those are what people look at once we graduate.

Acknowledgment

All the ideas in this blog originate from the talks with mentors of the YArch’19 workshop. Thanks to Prof. Boris Grot from the University of Edinburgh, Prof. Thomas Wenisch from the University of Michigan, Prof. Vijay Janapa Reddi from Harvard University, Prof. Luis Ceze from the University of Washington, and Prof. Kevin Skadron from the University of Virginia.

Thanks to two chairs of the YArch’19 workshop, Shaizeen Aga from AMD Research and Prof. Aasheesh Kolli from Pennsylvania State University, for making this possible.

Greg Chan and Bhargav Godala from the Liberty Research Group were at most of these talks and helped me write down some ideas.

6th year Ph.D. student @ Liberty Research Group, Princeton University

Graduated Master @ Liberty Research Group, Princeton University

Research guidance, Research Journals, Top Universities

Ph.D. Topics in Computer Science

While there are many topics, you should choose the research topic according to your personal interest. However, the topic should also be chosen on market demand. The topic must address the common people’s problems.

In this blog post, we are listing important and popular Ph.D. (Research) topics in Computer Science .

PhD in Computer Science 2023: Admission, Eligibility

Page Contents

The hottest topics in computer science

• Artificial Intelligence.
• Machine Learning Algorithms.
• Deep Learning.
• Computer Vision.
• Natural Language Processing.
• Blockchain.
• Various applications of ML range: Healthcare, Urban Transportation, Smart Environments, Social Networks, etc.
• Autonomous systems.
• Data Privacy and Security.
• Lightweight and Battery efficient Communication Protocols.
• Sensor Networks
• 5G and its protocols.
• Quantum Computing.
• Cryptography.

Cybersecurity

• Bioinformatics/Biotechnology
• Computer Vision/Image Processing
• Cloud Computing

Other good research topics for Ph.D. in computer science

Bioinformatics.

• Modeling Biological systems.
• Analysis of protein expressions.
• computational evolutionary biology.
• Genome annotation.
• sequence Analysis.

Internet of things

• adaptive systems and model at runtime.
• machine-to-machine communications and IoT.
• Routing and control protocols.
• 5G Network and internet of things.
• Body sensors networks, smart portable devices.

Cloud computing

• How to negotiate service level platform.
• backup options for the cloud.
• Secure data management, within and across data centers.
• Cloud access control and key management.
• secure computation outsourcing.
• most enormous data breach in the 21st century.
• understanding authorization infrastructures.
• cybersecurity while downloading files.
• social engineering and its importance.
• Big data adoption and analytics of a cloud computing platform.
• Identify fake news in real-time.
• neural machine translation to the local language.
• lightweight big data analytics as a service.
• automated deployment of spark clusters.

Machine learning

• The classification technique for face spoof detection in an artificial neural network.
• Neuromorphic computing computer vision.
• online fraud detection.
• the purpose technique for prediction analysis in data mining.
• virtual personal assistant’s predictions.

More posts to read :

• How to start a Ph.D. research program in India?
• Best tools, and websites for Ph.D. students/ researchers/ graduates
• Ph.D. Six-Month Progress Report Sample/ Format
• UGC guidelines for Ph.D. thesis submission 2021

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Doctoral Programs in Computational Science and Engineering

Doctor of philosophy in computational science and engineering, program requirements.

Programs Offered by CCSE in Conjunction with Select Departments in the Schools of Engineering and Science

The interdisciplinary doctoral program in Computational Science and Engineering ( PhD in CSE + Engineering or Science ) offers students the opportunity to specialize at the doctoral level in a computation-related field of their choice via computationally-oriented coursework and a doctoral thesis with a disciplinary focus related to one of eight participating host departments, namely, Aeronautics and Astronautics; Chemical Engineering; Civil and Environmental Engineering; Earth, Atmospheric and Planetary Sciences; Materials Science and Engineering; Mathematics; Mechanical Engineering; or Nuclear Science and Engineering.

Doctoral thesis fields associated with each department are as follows:

• Aerospace Engineering and Computational Science
• Computational Science and Engineering (available only to students who matriculate in 2023–2024 or earlier)
• Chemical Engineering and Computation
• Civil Engineering and Computation
• Environmental Engineering and Computation
• Computational Materials Science and Engineering
• Mechanical Engineering and Computation
• Computational Nuclear Science and Engineering
• Nuclear Engineering and Computation
• Computational Earth, Science and Planetary Sciences
• Mathematics and Computational Science

As with the standalone CSE PhD program, the emphasis of thesis research activities is the development of new computational methods and/or the innovative application of state-of-the-art computational techniques to important problems in engineering and science. In contrast to the standalone PhD program, however, this research is expected to have a strong disciplinary component of interest to the host department.

The interdisciplinary CSE PhD program is administered jointly by CCSE and the host departments. Students must submit an application to the CSE PhD program, indicating the department in which they wish to be hosted. To gain admission, CSE program applicants must receive approval from both the host department graduate admission committee and the CSE graduate admission committee. See the website for more information about the application process, requirements, and relevant deadlines .

Once admitted, doctoral degree candidates are expected to complete the host department's degree requirements (including qualifying exam) with some deviations relating to coursework, thesis committee composition, and thesis submission that are specific to the CSE program and are discussed in more detail on the CSE website . The most notable coursework requirement associated with this CSE degree is a course of study comprising five graduate subjects in CSE (below).

Computational Concentration Subjects

Note: Students may not use more than 12 units of credit from a "meets with undergraduate" subject to fulfill the CSE curriculum requirements

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Academics | PhD Program

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The PhD degree is intended primarily for students who desire a career in research, advanced development, or teaching. A broad Computer Science, Engineering, Science background, intensive study, and research experience in a specialized area are the necessary requisites.

The degree of Doctor of Philosophy (PhD) is conferred on candidates who have demonstrated to the satisfaction of our Department in the following areas:

• high attainment in a particular field of knowledge, and
• the ability to do independent investigation and present the results of such research.

They must satisfy the general requirements for advanced degrees, and the program requirements specified by our Department.

Program Requirements

On average, the program is completed in five to six years, depending on the student’s research and progress.

Progress Guidelines

Students should consider the progress guidelines to ensure that they are making reasonable progress.

Monitoring Progress

Annual reviews only apply to PhD students in their second year or later; yearly meetings are held for all PhD students.

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Best Doctorates in Computer Science: Top PhD Programs, Career Paths, and Salaries

Getting a PhD in the field of computer science is the best way to influence the future of technological innovation and research. If you are interested in getting a computer science doctoral degree, then our list of the best PhDs in Computer Science will help you find the program that caters most to your goals.

A PhD in Computer Science can branch out into a wide variety of science and tech fields. Be it information assurance, computational science theory, or cyber operations, you can specialize your computer science PhD to suit your interests. In our guide, we’ve also gone into detail about the average PhD in Computer Science salary and the best computer science jobs PhD students can get.

Find your bootcamp match

What is a phd in computer science.

A PhD in Computer Science is a doctoral degree where graduate students perform research and submit original dissertations covering advanced computing systems topics. Computer science is a broad field that covers artificial intelligence, operating systems, software engineering, and data science.

Your doctoral dissertation will include a research proposal, coursework in advanced topics related to computer science, and a thesis presentation. The wide span of this field allows you to choose a PhD program that can cover topics in any high-performance computing systems area.

How to Get Into a Computer Science PhD Program: Admission Requirements

The admissions requirements to get into a computer science PhD program include submitting your official transcripts from your undergraduate or graduate programs and resume. Your previous university coursework should showcase a strong background in software development, popular programming languages , and scientific computing.

Universities also usually require the submission of your GRE score. A combined score of 1,100 is typically where you want to be when applying to PhD programs. You’ll also usually be required to submit three or more letters of recommendation and a personal essay stating your thesis or research proposal. Keep in mind that each university’s admissions requirements will vary.

PhD in Computer Science Admission Requirements

• 3.0 or higher cumulative GPA
• Three letters of recommendation
• Official transcript from your undergraduate degree or your graduate degree
• Prerequisite courses covering computer science academic programs
• Personal statement highlighting proposal of thesis or research topic

Computer Science PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Computer Science?

It is very hard to get into a PhD program in computer science. This is because prospective students need to meet a very competitive GPA, have an excellent academic background, and fulfill other advanced program requirements. Your chances of getting accepted into a computer science doctorate degree program will typically range between 10 to 20 percent.

In fact, less than 10 percent of computer science graduate applicants are accepted at the University of California. Similarly, Duke University reports that only around 15.7 percent of applicants were selected for its 2021 to 2022 computer science PhD program. Your acceptance relies on submitting a compelling thesis proposal statement that displays your passion and high academic competency.

How to Get Into the Best Universities

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Best PhDs in Computer Science: In Brief

Best Universities for Computer Science PhDs: Where to Get a PhD in Computer Science

The best universities for computer science PhDs are Arizona State University, Boston University, Harvard University, Duke University, and Carnegie Mellon University. Each of these universities will help you advance your research and eventually get you a job in artificial intelligence , software development, or computing systems. We’ve also broken down the application process and other details for each program.

According to the US News & World Report, Arizona State University ranks number one on the list of the most innovative schools and number 36 in the best undergraduate engineering programs. It was founded in 1885 and currently offers over 450 graduate programs and employs more than 340 PhD fellows. 

PhD in Computer Science 

Arizona State University offers research opportunities in the fields of artificial intelligence, cyber security, big data, or statistical modeling under the umbrella of this computer science program. In this 84-credit program, you’ll tackle your dissertation, prospectus, and oral and written exams. You’ll also take courses on computational processes, information assurance, and network architecture. 

Your PhD dissertation includes 12 credit hours of experience culmination that can be planned alongside your research and elective credits. This degree is best suited for computer scientists wanting to build a career in machine learning or an academic career. 

PhD in Computer Science Overview

• Program Length: 4 to 6 years
• Acceptance Rate: N/A
• Tuition and Fees: $6,007/semester, nine credits or more (in state); $1,663/hour, under 12 credits or $16,328 per semester, 12 credits or more (out of state) 
• PhD Funding Opportunities: Teaching assistantships, research assistantships
• Three letters of recommendations from former professors or employers 
• One to two-page statement of purpose that covers previous research experiences and reasoning behind your interest in one to two doctoral programs
• Optional submission of GRE scores. Preferred scores are 146 verbal, 159 quantitative, and 4.0 analytical writing
• Official transcripts
• Bachelor’s Degree in Computer Science or computer engineering. Applicants with a master’s degree in a relevant field are preferred 
• Minimum 3.5 cumulative GPA

Founded in 1839, Boston University is a top private research university with a reputable engineering and technology program. It offers over 350 graduate programs and PhDs in topics such as neurobiology, biostatistics, computer engineering, mathematical finance, and systems engineering. 

PhD in Computer Science

If you are interested in advancing in research and academia, then this PhD program is worth looking into. Its curriculum trains you to build a successful professional background in the intelligent control systems, cloud infrastructures, and cryptography fields. Candidates need to clear its qualification, dissertation, and milestone requirements to complete this degree. 

• Program Length: 5 to 6 years
• Acceptance Rate: 10%
• Tuition and Fees: $61,924/year
• PhD Funding Opportunities: Computer Science Fellowship, Teaching Excellence Award, Research Excellence Award, Teaching Fellow Expectations 
• GRE scores normally mandatory, but are optional for fall 2022
• A personal statement stating your interest in the program 
• Resume 

Carnegie Mellon University is a globally recognized university with more than 14,500 students and over 109,900 alumni. The school was founded in the year 1900 and offers over 80 majors and minors. According to the US News & World Report, Carnegie Mellon University ranks number one on the best undergraduate computer science program in the country. 

This on-campus PhD program focuses on computing research, software informatics, and communication technologies. Completing this doctoral degree program will open you up to a wide range of career prospects across the data science, computing technology, and information technology research fields. 

This degree includes 24 units of advanced computing research, 72 units of graduate courses, and the dissertation process of an original research thesis. This PhD is apt for those looking to establish their career in research and academia. During this program, you’ll also serve as a teaching assistant in the computer science department twice as per the degree requirement. 

• Acceptance Rate: 5% to 10%
• Tuition and Fees: $75,272/year 
• PhD Funding Opportunities: Internal funding, external funding, dependency allowance, fellowships
• GRE scores optional but encouraged
• Most recent transcript of the university attended
• One to two-page statement of purpose stating your interest in the program, research interests, PhD objective, and relevant experience
• Three letters of recommendation from previous faculty or employers   

Duke University was established in 1924 and counts among the top universities in the world. It has an undergraduate population of 6,789 and a graduate population of 9,991 students and is most recognized for its computer science, biology, public policy, and economics departments. It offers over 80 doctoral and master’s degrees covering STEM, social sciences, and humanities. 

This computer science PhD is definitely worth it for doctorate students looking to embark on an advanced computer science research path. In it, students tackle a research initiation project, preliminary exam, dissertation process, and core qualification credits. Doctoral candidates are also required to partake in the department’s teaching assistantship program. 

Its curriculum includes core courses in computation theory, artificial intelligence, algorithms, numerical analysis, and computer architecture. Graduates of the program open themselves up to numerous career opportunities across a wide range of computing systems academic and research fields. 

• Program Length: 3 to 4 years
• Acceptance Rate: 15.7%
• Tuition and Fees: $70,185/year for the first three years and $18,165/year each subsequent year
• PhD Funding Opportunities: Teaching assistantships, research assistantships, fellowships
• Official transcripts from all attended universities 
• Statement of purpose
• GRE scores are optional for 2022 but recommended 
• No minimum GPA requirements but high GPA scores are preferred

Harvard University is a top Ivy League institution that has amassed global recognition and top rankings in many of its departments. Founded in 1636, the university is home to many excellent programs across the fields of law, medicine, economics, and computer science. It has more than 400,000 alumni and a total enrollment of 35,276 students. 

According to the US News & World Report, Harvard University ranked number one among the best global universities in 2022 . Its graduate schools offer doctorate programs in the applied sciences, biology, literature, environmental sciences, business, and healthcare fields. 

Attending a computer science PhD program at Harvard University brings high credibility and accolades to your professional candidacy. This program is offered by the university’s Graduate School of Arts and Sciences and provides focus opportunities across the engineering science, applied physics, computer science, and applied mathematics areas.  

Similar to most mainstream PhDs, this program requires the completion of 10 semester-long graduate courses, a dissertation topic, oral and written qualifying exams, a teaching assistantship, and a defense process. After graduating, you’ll easily qualify for some of the most prestigious research and career opportunities available.

• Program Length: 3 or more years
• Acceptance Rate: 6%
• Tuition and Fees: $50,928 for the first two years and $13,240 reduced tuition for the third and fourth year
• PhD Funding Opportunities: Teaching fellowships, research assistantships, GSAS fellowships, external funding 
• Supplemental form for PhD
• Transcripts from all post-secondary education 
• Statement of purpose stating your interest in the program  

Oregon State University is a public research university founded in 1868 with over 210,000 alumni. The school is home to more than 28,607 undergraduate and 5,833 graduate students and offers over 300 academic programs as well as a robust research department. Its doctoral programs can be found in the business, agricultural science, education, engineering, or medicine departments. 

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This PhD is offered by the university’s electrical engineering and computer science department and is perfect for doctoral candidates wanting to work in IT research in the governmental or educational sectors. The program offers research opportunities in topics such as data science, cyber security, artificial intelligence, computer graphics, and human-computer interaction. 

The program’s curriculum includes graduate-level courses in theoretical computer science and requires the completion of your research thesis. You’ll also be required to maintain an overall cumulative GPA of 3.0 and pass all preliminary and oral exams to receive your PhD. 

• Program Length: 4 years
• Tuition and Fees: $557/credit (in state); $1,105/credit (out of state)
• PhD Funding Opportunities: Graduate teaching assistantship, research assistantship, Outstanding Scholars Program
• Three letters of recommendation from previous professors or employers familiar with your technical skills 
• Transcripts and academic history of all attended universities 
• Minimum 3.0 GPA in the last two years of your undergraduate or graduate work 
• Statement of objective listing your interest in the program, career goals, research interests, and relevant experience

Syracuse University is a private institution that was established in 1870 and is most popular for its research and professional training academic programs. It has more than 40 research centers focusing on the STEM, social sciences, and humanities fields. The university has over 400 majors, minors, and advanced degrees its students can choose from. 

It had a total enrollment of 14,479 undergraduate students and 6,193 graduate students in the fall of 2020. Prospective students can pick a PhD focus from many of its applied topics, including data science, statistics, human development, and bioengineering. 

PhD in Computer and Information Science and Engineering

A PhD focused in computer and information science and engineering from Syracuse University can help you advance your career in the information technology, software engineering, or information assurance fields. This program is best suited for computing technology research buffs looking to land senior-level positions in the field. 

The program’s curriculum is an amalgamation of graduate coursework, your dissertation and research presentation, and exams. Your coursework will cover technical topics ranging from algorithms and artificial intelligence to operating systems and hardware systems. 

PhD in Computer and Information Science and Engineering Overview

• Program Length: 4 to 5 years
• Acceptance Rate: 14.28%
• Tuition and Fees: $32,110/year 
• PhD Funding Opportunities: Research assistantships, departmental teaching assistantships, university fellowships

PhD in Computer and Information Science and Engineering Admission Requirements

• Minimum GRE scores: Verbal 153, Quantitative 155, and analytical writing 4.5 
• Bachelor of Science or Master of Science in computer engineering, electrical engineering, or computer and information science
• Two or more letters of recommendation from previous faculty or employers 
• Official transcripts of all attended universities 
• 500-word personal statement concerning your interest in the program

The University of Oklahoma is a public school best known for its business, journalism, and petroleum engineering programs. Founded in 1890, it currently has an undergraduate student population of 21,844 and offers over 170 academic programs and graduate degrees in a wide range of subject areas. 

The school’s doctoral topics are numerous and can be found within its business, architecture, fine arts, education, engineering, journalism, or geographics science departments. The University of Oklahoma is also incredibly well known for its athletic programs, having won many national championships.

The university’s computer science PhD has courses in machine learning, data science, computer security, visual analytics, database management, and neural networking subjects. If you’re interested in a data science, network security, artificial intelligence, or cyber security career, then this PhD is for you.

The program allows you to propose a research topic covering anything in the field of advanced computing systems and theories. During your program, you’ll undergo an annual research progress review along with general examinations until your defense. The program also requires you to submit a minimum of two publications before you complete your degree. 

• Program Length: 6 years
• Tuition and Fees: $591.90/credit (in state); $1,219.50/credit (out of state)
• PhD Funding Opportunities: Graduate assistantships, research assistantships, fellowships, scholarships, research grants
• Prerequisite coursework covering computer science, data structures, and math subjects 
• Bachelor’s degree or master’s degree
• Minimum cumulative 3.0 GPA 
• 250-word statement of purpose concerning your interest and goals in the program 
• Three letters of recommendation, with two of them preferably from previous professors

The University of Arizona was founded in 1885 and is a public research institution with over 300 major programs. The school is home to 36,503 undergraduate and 10,429 graduate students and offers PhD programs in over 150 areas of study, including information science, statistics, mechanical engineering, biomedical science, medicine, communication, and economics. 

If you want to become an applications architect or pursue a career in academia focusing on computing or business intelligence technologies, then this PhD is for you. It offers courses in computer networking, system architecture, database systems, machine learning theory, natural processing language, and computer vision. 

The program’s curriculum requires the completion of 12 units of advanced computer science research and 18 units of dissertation presentation and defense. You’ll also need to maintain a minimum cumulative GPA of 3.33 to receive your PhD. 

• Program Length: 5.5 years
• Acceptance Rate: 17.73%
• Tuition and Fees: $989.12/unit (in state); $1,918.12/unit (out of state)
• PhD Funding Opportunities: Graduate assistantships, graduate associate fund, teaching assistantships, research assistantships, graduate college fellowship
• Official transcripts from all attended universities
• Minimum of two letters of recommendation by previous faculty or employers 
• A statement of purpose stating your interest in the school and the program faculty, your career goals, preferred research areas, and research background
• Resume detailing previous research work, published papers, conference presentations, and computer science background 
• Bachelor’s degree in computer science or a related field 
• A background in operating systems, programming languages, discrete mathematics, data structures, and theory of computation 
• Minimum 3.5 undergraduate GPA and 3.7 graduate GPA 

The University of Maryland is a research-focused institution that was founded in 1856. It hosts more than 41,200 students and offers over 217 undergraduate and master’s programs. It also offers 84 doctoral programs and has an extensive research department. According to the US News & World Report, the school ranks number 20 among the top public schools in the country .

This PhD program offers research opportunities in subjects such as robotics, big data, scientific computing, machine learning, geographic information systems, and quantum computing. Doctoral students can participate in a collaborative research journey at any of the school’s research specialized institutions. The program curriculum includes graduate coursework, a research proposal, and a dissertation defense. 

• Tuition and Fees: $11,586/year (in state); $24,718/year (out of state) 2022-2023
• PhD Funding Opportunities:  Research assistantships, departmental teaching assistantships, National Science Foundation Graduate Fellowships, Fulbright Fellowships
• Transcripts from all attended universities
• Writing sample and optional publications or presentations 
• Statement of purpose concerning your interests in the field and program 
• Three letters of recommendation 

Can You Get a PhD in Computer Science Online?

Yes, you can get a PhD in Computer Science online. An online doctoral degree will be more course-based instead of research-based due to the lack of laboratory facilities. Computer science is a broad field that offers doctoral opportunities across a wide range of tech topics. You can get an online PhD in information science, data science, data analytics, or information systems.

Know that online PhDs are rare across most fields, including computer science. Obtaining a non-research-focused doctoral degree won’t be as respected as a traditional computer science PhD. The online PhD programs listed below are best suited for candidates looking to advance into managerial, theoretical research, and academic positions in the technology sector.

Best Online PhD Programs in Computer Science

How Long Does It Take to Get a PhD in Computer Science?

It takes an average of four years to get a PhD in Computer Science. However, the actual duration is entirely dependent on the candidate’s research proposal approval and defense success, and depending on your research pace, it can take up to five or six years to complete. The graduate course portion of your degree is the most straightforward and typically takes around 2.5 years to complete.

Your dissertation topic selection, research journey, publication submissions, and defense presentations will take the most amount of time, usually between three to five years. Some universities also require their PhD students to complete a minimum of two years of graduate teaching assistantship. An online PhD in Computer Science usually only takes three years to finish, as it mostly includes advanced coursework.

Is a PhD in Computer Science Hard?

Yes, a PhD in Computer Science is hard. Computer science is a complex field that incorporates an array of advanced technical topics. Your PhD will require you to submit an original research proposal on an advanced information technology subject such as data science, machine learning, quantum computing, artificial intelligence, and network security topics.

Along with advanced research and a dissertation, you’ll also need to complete advanced graduate courses with a minimum GPA of 3.0. Other requirements often include submitting one or more publications, working in graduate teaching positions, and successfully defending your thesis topic. The combination of all of these academic requirements makes getting a PhD in Computer Science a hard process.

How Much Does It Cost to Get a PhD in Computer Science?

It costs $19,314 per year to get a PhD in Computer Science, according to the National Center for Education Statistics (NCES). However, your total PhD tuition can vary depending on a number of factors, including the university’s ranking, the program’s timeline, and the PhD funding opportunities you’ll have available.

The NCES further categorizes the graduate program tuition according to the institution type and reports that the average fee for public institutions was $12,171 from 2018 to 2019. It also states that private for-profit institutions charged an average of $27,776, and non-profit schools charged $14,208 those same years.

How to Pay for a PhD in Computer Science: PhD Funding Options

The PhD funding options that students can use to pay for a PhD in Computer Science include graduate research assistantships, teaching assistantships, and fellowship opportunities. Your funding options will vary from school to school and can include both external and internal funding.

Some of the popular ways to fund your PhDs include research grants, federal work-study programs, teaching or graduate assistantships, tuition waivers, and graduate research fellowships. You can also apply for scholarships or tuition reimbursement options at your current job. Your graduate advisor and computer science faculty can help you find more funding options.

Best Online Master’s Degrees

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What Is the Difference Between a Computer Science Master’s Degree and PhD?

The difference between a computer science master’s degree and a PhD is the level of each degree. A Master’s Degree in Computer Science is a typical precursor to a PhD and covers the technical field less extensively than a doctoral program. It will last around two to three years and can be fully course-based or thesis-based.

A PhD in Computer Science provides you with higher qualifications and more research and dissertation autonomy. It can last anywhere between four to six years and gives you original publication and research credibility. Both of these computer science degrees are considered graduate degrees, but a PhD provides you with a higher educational accolade.

Master’s vs PhD in Computer Science Job Outlook

The job outlook for a professional with a master’s vs PhD in Computer Science will generally coincide as most senior-level careers can be achieved with a master’s degree. According to the US Bureau of Labor Statistics (BLS), the job outlook for computer and information research scientists is projected to grow by 22 percent between 2020 and 2030.

This job typically requires a master’s degree meaning PhD holders also qualify and can apply for it. The commonality of these job growth statistics also applies to other tech positions, including information security scientists and network architects. That being said, the specific growth rate of your job will also vary depending on your career choice.

For example, university computer science professor positions, which typically only computer science PhD holders are eligible for, have a projected growth rate of 12 percent between 2020 and 2030, according to the BLS. With computer science professionals being high in demand, most PhD in Computer Science jobs have a positive projected growth rate.

Difference in Salary for Computer Science Master’s vs PhD

The difference in salary for computer science master’s vs PhD grads can vary depending on their position and place of employment. According to PayScale, the average salary for a computer science PhD holder is $131,000 per year , which is higher than the average salary of a master’s degree graduate.

According to PayScale, the average salary for a computer science master’s graduate is $105,000 per year . The salary disparity with these degrees stems from the differences in their level of seniority, industry experience, and educational accolades.

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Why You Should Get a PhD in Computer Science

You should get a PhD in Computer Science because it is an advanced and highly reputable degree that will help you land senior technical, academic, and research roles. A PhD is a gateway to a lucrative and innovative technology career, allowing you to follow your research passion across the fields of artificial intelligence, data science, or computing theory.

Reasons for Getting a PhD in Computer Science

• Extensive and advanced research opportunities. A PhD in Computer Science covers many advanced computing science fields. You can learn specialized skills through your research opportunities and eventually work in advanced data science, artificial intelligence, neural networking, information technology, or computing theory.
• Higher salary. PhD graduates qualify for career opportunities working in senior positions as scientists, professors, managers, or heads of departments. These senior positions come with high compensation and job security.
• Rewarding education. A computer science PhD is perfect for those who are interested in contributing toward leading innovation and technology research. As a doctoral student, you can propose and conduct advanced research in the field while contributing to today’s technological growth.
• Increased job candidacy. Having a computer science PhD on your resume and portfolio will enhance your candidacy when applying to tech positions across all industries. A PhD is a highly reputable degree that demonstrates your expertise in the field and ultimately makes you a highly sought-after candidate.

Getting a PhD in Computer Science: Computer Science PhD Coursework

The graduate requirements for getting a PhD in Computer Science and most common PhD coursework are different from program to program and are heavily dependent on your specialization, but often have some commonalities. Here are some examples of courses you may take during your PhD.

System Architecture

A systems architecture course in a computer science PhD covers advanced operating systems, communication technologies, network security, and computer architecture. You’ll also take classes covering topics like network systems and software engineering.

Artificial Intelligence

Artificial intelligence is a rapidly growing field that is integral to the field of computer science and data science. Your program will cover the latest artificial intelligence technologies and research areas such as deep learning, interactive systems, neural networking, and artificial intelligence infrastructure.

Information Assurance

Network security, information assurance, and cyber security are also part of an extensive education coverage of the computer science field. This course will cover vital knowledge concerning information security, system integrity, data privacy, and system authentication.

Data science courses in a computer science PhD program cover topics such as big data, database management, data analytics, data mining, and machine learning subjects. You will learn about data science processes and methods as well as the tools and technologies used in advanced data engineering.

Theory of Computation

A theory of computation course will teach you advanced algorithms, computation models, Turing machines, quantum computing, and automata theories. You’ll also have lessons that cover the Godel Incompleteness theorem and molecular computing.

Best Master’s Degrees

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How to Get a PhD in Computer Science: Doctoral Program Requirements

If you are wondering how to get a PhD in Computer Science and complete the doctoral program requirements, this section will provide you with the answers you’re looking for. The graduation and academic requirements will vary from one PhD program to another, but there are some common requirements across all computer science departments. Here are some of them.

A computer science PhD is an amalgamation of graduate-level courses and research. All PhDs will require you to complete their graduate course requirements which cover topics like data science, computing systems, artificial intelligence, and information assurance. The required number of courses will vary depending on the program but is typically between 10 and 15. 

Maintaining a minimum required cumulative GPA in your courses is a requirement across all PhD programs. The GPA requirement can range anywhere from 3.0 to 3.5. This is one of the major ways your program department tracks your progress and whether or not you are struggling with the work.

Clearing the qualifying exams with a passing grade while maintaining the required GPA is another PhD graduation requirement. Your preliminary exam is a public presentation discussing your research topics with approval committees and other students. Written exams and oral exams come with each course and are a test of your computer science and tech abilities.  

You are typically required to present your research proposal or research initiation project within the first two years of your PhD. You must get your research idea approved by the approval committee and begin the research process within those two years. 

Once you embark on your computer science research process, you are required to present an annual progress report. This presentation is a review process where the approval committee will ask questions and provide feedback on your progression.  

Your PhD milestones may also include publication requirements. For these, you’ll be required to submit one or two peer-reviewed journal or publication entries covering the computer science topics you are researching. 

Universities also require PhD candidates to complete two years of graduate teaching assistantships or research assistantships. These assistantships are one of the best ways to secure funding for your PhD program. 

Getting your dissertation approved and completing your research and thesis is one of the most important milestones of your PhD. Your assigned research committee, thesis advisor, and approval committee will need to approve your research and dissertation for your to be able to graduate. 

Computer science PhDs will have a timeline breakdown that candidates are expected to meet. You will typically need to complete the graduate coursework within two to three years and complete your dissertation and thesis within six years. You can request a timeline extension with your advisor’s approval.

The thesis for your PhD in Computer Science will cover your chosen research subject area. It will include a thesis proposal submission, thesis presentation, and thesis approval process as well as an extensive written document covering your hypothesis, findings, and conclusions. 

Potential Careers With a Computer Science Degree

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PhD in Computer Science Salary and Job Outlook

The salary and job outlook for a PhD in Computer Science will vary according to your job designation but are generally positive. The average salary for some of the highest-paid jobs will range between $86,712 and $179,351. Below are some of the most lucrative career paths a computer science PhD holder can embark on.

What Can You Do With a PhD in Computer Science?

You can work in a wide range of advanced technical positions with a PhD in Computer Science. This doctoral degree qualifies you for positions as a manager, scientist, college professor, and researcher. You could lead an information assurance department or become a computer science professor, chief data scientist, or artificial intelligence researcher.

Best Jobs with a PhD in Computer Science

• Computer Research Scientist
• Computer Science Professor
• Research and Development Lead
• Computer Systems Engineer
• Information Technology Manager

What Is the Average Salary for a PhD in Computer Science?

The average salary for someone with a PhD in Computer Science is $131,000 per year , according to PayScale. Your actual salary will vary depending on your specific position, location, and experience. In fact, with a PhD, you could work as a chief data scientist and make between $136,000 and $272,000 or as a senior software engineer and make $104,000 to $195,000.

Highest-Paying Computer Science Jobs for PhD Grads

Best Computer Science Jobs with a Doctorate

The best computer science jobs with a doctorate degree all earn a high salary and have high projected growth in the next few years. These jobs cover a wide range of computer science disciplines, meaning that you’ll easily be able to find a position doing something you enjoy.

A chief data scientist is in charge of the data analytics and data science departments of an organization. They are responsible for the approval of new database system designs, data strategies, and data management decisions. 

• Salary with a Computer Science PhD: $179,351
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, Washington

A chief information officer is an IT executive responsible for managing and overseeing the computer and information technology departments of a company. Also known as CTOs, they are responsible for delegating tasks and approving innovation and technology upgrade ideas proposed by their teams. 

• Salary with a Computer Science PhD: $168,680
• Job Outlook: 11% job growth from 2020 to 2030
• Number of Jobs: 482,000
• Highest-Paying States: New York, California, New Jersey, Washington, District of Columbia

A senior computer scientist heads the research department of a computer science, artificial intelligence, or computer engineering field. These professionals, along with their research team, are tasked with developing efficient and optimal computer solutions across a wide range of sectors. 

• Salary with a Computer Science PhD: $153,972

An IT security architect is a cyber and information security professional responsible for developing, maintaining, and upgrading the IT and network security infrastructure of a business or organization. Additionally, they oversee an organization’s data, communication systems, and software systems security aspects. 

• Salary with a Computer Science PhD: $128,414
• Job Outlook : 5% job growth from 2020 to 2030
• Number of Jobs: 165,200
• Highest-Paying States: New Jersey, Rhode Island, Delaware, Virginia, Marlyand

A computer science professor is a university professor who educates college students concerning basic and advanced computer science subjects. They are responsible for creating and instructing a course curriculum as well as testing their students. Some computer science professors also work as research faculty at a university. 

• Salary with a Computer Science PhD: $86,712
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900 
• Highest-Paying States: California, Oregon, District of Columbia, New York, Massachusetts

Is a PhD in Computer Science Worth It?

Yes, a PhD in Computer Science is worth it for anyone wanting to work in senior professions in the field of technology. This doctoral degree opens its recipients up to numerous career opportunities across academia, research and development, technology management, and chief technical positions.

Getting a computer science PhD equips you with specialized skills and extensive research capabilities. During your studies, you’ll get the opportunity to contribute to the rapidly developing world of technology with your original dissertation and specialize in data science, network security, or computing systems.

Additional Reading About Computer Science

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PhD in Computer Science FAQ

The preferred GPA for a computer science PhD is 3.5 or above. Keep in mind that meeting the minimum requirement doesn’t guarantee acceptance. The higher you can get your GPA during your bachelor’s and master’s, the more likely it is you will be accepted to the PhD program of your choice.

The standardized exam you need to take to get a PhD in Computer Science is the Graduate Record Examination (GRE). The GRE score requirements will vary from university to university and several schools have currently waived GRE requirements due to the coronavirus pandemic.

You can choose from a wide range of potential research subjects for your computer science PhD, including computer algorithms, data science, artificial intelligence , or cyber security. You can also research business process modeling, robotics, quantum computing, machine learning, or other big data topics.

You can get into a computer science PhD program by impressing the admissions committee and the school’s computer science graduate department with your skills, experience, grades, and desired research topic. Students with a 3.5 or higher GPA, a high GRE score, extensive IT skills, and an impressive research topic have a higher chance of admission.

About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Learn about the CK publication .

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PhD Assistance

How to select the right topic for your phd in computer science, introduction  .

Starting a PhD in Computer Science is an exciting but demanding effort, and choosing the correct computer science research topics is critical to a successful and rewarding experience. This critical decision not only influences the course of your academic interests, but also the effect of your contributions to the field. In this blog, we will look at crucial factors to consider when selecting a research subject, such as connecting with your passion, discovering gaps in current literature, and determining the feasibility of the project. By navigating this process with awareness and strategy, you will be able to begin a meaningful and effective doctorate research path in the dynamic field of computer science.  

• Check our PhD Topic selection examples to learn about how we review or edit an article for Topic selection.  

PhD in computer science is a terminal degree in computer science along with the doctorate in Computer Science, although it is not considered an equivalent degree. Computer science deals with algorithms and data and the computation of them via hardware and software, the principles and constraints involved in the implementation. Choosing a topic for research in computer science can be tricky. The field is as vast as its parent field, mathematics. Taking into account certain factors before choosing a topic will be helpful: it is preferable to choose a topic which is currently being studied by other fellow researchers, this will help to establish bonds and sharing secondary data. Finding a topic that will add value to the field and result in the betterment of existing processes will cement your legacy within the field and will also be helpful in getting funds. Always choose a topic that you are passionate about. Your interest in the topic will help in the long run; PhD research is a long, exhausting process and computational researches will dry you out. If you have an area of interest, read about the existing developments, processes, researches. Reading as much literature as possible will help you identify certain or several research gaps. You can consult with your mentor and choose a particular gap that would be feasible for your research. An extension of the previous method of spotting a research gap is to build on references for future research given in existing dissertations by former researchers. You can be critical of existing limitations and study it.

Besides, there are plenty of enigmatic areas in computer science. The unsolved questions within computer science plenty which you can study and find a solution to build on the existing body of knowledge. Major titles with unsolved questions for research in Computer Science

Computational complexity

The process of arranging computational process according to complexity based on algorithm has had various problems that are unsolved. This includes the Classic P versus the NP, the relationship between NQP and P, NP not known to be P or NP-complete, unique games conjecture, separations between other complexity cases, etc.

Polynomial versus non-polynomial time for specific algorithmic problems

A continuation in computational complexity is the complex case of NP- intermediate which contains within numerous unsolved problems related to algebra and number theory, Boolean logic, computational geometry, and computational topology, game theory, graph algorithm, etc.

Algorithmic problems

Scores of questions within the existing algorithm in computer science can be improved with new processes.

Natural Language Processing algorithms

Natural language processing is an important field within computer science with the onset of deep learning and Artificial and Intelligence. Plenty of researches are being carried in the field to find faster and perfect ways to syllabify, stem, and POS tag algorithms specifically for the English language.

Programming language theory

The case for scope of research about programming language within computer science is evergreen. There are always ways to design, implement, analyze, characterize, and classify programming languages and to develop newer languages.

• Check out our study guide to learn more about How to Select the Best Topics for Research?  

Conclusion:  

In conclusion, the journey of selecting the right PhD topic in computer science topics is a pivotal phase requiring careful deliberation. By combining passion, alignment with current computer science phd topics trends, and feasibility assessment, one can pave the way for a successful and rewarding research endeavor. Remember, the chosen topic will not only define your academic trajectory but also contribute to the evolving landscape of computer science thesis topics. Embrace the challenge with purpose, stay adaptable, and ensure that your research aligns with both personal interests and the broader needs of the field. With these considerations, you are poised to make a lasting impact in the world of Computer Science.  

Example Research Topics in Technology and Computer Science    

• Role of human-computer interaction   
• AI and robotics   
• Software engineering and programming   
• Machine learning and neuron networks  

About PhD Assistance  

At PhD Assistance , we have a team of trained research specialists with topic selection experience. Our writers and researchers have extensive expertise in selecting the appropriate topic and title for a PhD dissertation based on their Specialized subject and personal interests. Furthermore, our professionals are drawn from worldwide and top-ranked colleges in nations such as the United States, United Kingdom, and India. Our writers have the expertise and understanding to choose a PhD research subject that is actually excellent for your study, as well as a snappy title that is unquestionably appropriate for your research aim.  

In summary, it is important to keep in mind the following to choose an apt topic for your PhD research in Computer Science:

Your passion for an area of research

Appositeness of the topic

Feasibility of the research with respect to the availability of the resource

Providing a solution to a practical problem.

Topic selection help for computer science students  

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PhD Computer Science / Overview

Year of entry: 2024

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The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD (or international equivalent).

Other combinations of qualifications and research or work experience may also be considered. Please contact the admissions team to check.

Full entry requirements

Apply online

In your application you’ll need to include:

• The name of this programme
• Your research project title (i.e. the advertised project name or proposed project name) or area of research
• Your proposed supervisor’s name
• If you already have funding or you wish to be considered for any of the available funding
• A supporting statement (see 'Advice to Applicants' for what to include)
• Details of your previous university level study
• Names and contact details of your two referees.

Find out how this programme aligns to the UN Sustainable Development Goals , including learning which relates to:

Goal 4: Quality education

Goal 8: decent work and economic growth, goal 9: industry, innovation and infrastructure, goal 17: partnerships for the goals, programme options.

Programme description

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The PhD is a three-year (or six year, if taken part-time) degree resulting in a substantial thesis.

The Department of Computer Science is one of the largest in the UK covering a huge spectrum of Computer Science topics. We currently have research groups ranging from Advanced Processor Technologies to Text Mining.

Our core Computer Science research is augmented by interdisciplinary research taking place at the interface with discipline areas including mathematics, physics, medicine and biology.

A detailed overview of the Department's research groups and core and interdisciplinary research themes is available in the 'research' area of our website and you can identify a possible project from our list of available projects .

For entry in the academic year beginning September 2024, the tuition fees are as follows:

• PhD (full-time) UK students (per annum): Band A £4,786; Band B £7,000; Band C £10,000; Band D £14,500; Band E £24,500 International, including EU, students (per annum): Band A £28,000; Band B £30,000; Band C £35,500; Band D £43,000; Band E £57,000
• PhD (part-time) UK students (per annum): Band A £2393; Band B £3,500; Band C £5,000; Band D £7,250; Band E 12,250 International, including EU, students (per annum): Band A £14,000; Band B £15,000; Band C £17,750; Band D £21,500; Band E £28,500

Further information for EU students can be found on our dedicated EU page.

The programme fee will vary depending on the cost of running the project. Fees quoted are fully inclusive and, therefore, you will not be required to pay any additional bench fees or administration costs.

All fees for entry will be subject to yearly review and incremental rises per annum are also likely over the duration of the course for Home students (fees are typically fixed for International students, for the course duration at the year of entry). For general fees information please visit the postgraduate fees page .

Always contact the Admissions team if you are unsure which fees apply to your project.

Scholarships/sponsorships

There are a range of scholarships, studentships and awards at university, faculty and department level to support both UK and overseas postgraduate researchers.

To be considered for many of our scholarships, you’ll need to be nominated by your proposed supervisor. Therefore, we’d highly recommend you discuss potential sources of funding with your supervisor first, so they can advise on your suitability and make sure you meet nomination deadlines.

For more information about our scholarships, visit our funding page or use our funding database to search for scholarships, studentships and awards you may be eligible for.

UN Sustainable Development Goals

The 17 United Nations Sustainable Development Goals (SDGs) are the world's call to action on the most pressing challenges facing humanity. At The University of Manchester, we address the SDGs through our research and particularly in partnership with our students.

Led by our innovative research, our teaching ensures that all our graduates are empowered, inspired and equipped to address the key socio-political and environmental challenges facing the world.

To illustrate how our teaching will empower you as a change maker, we've highlighted the key SDGs that our programmes address.

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all

Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation

Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development

Contact details

The School of Engineering creates a world of possibilities for students pursuing skills and understanding. Through dynamic research and teaching we develop engineering solutions that make a difference to society in an ethical and sustainable way.  Science-based engineering is at the heart of what we do, and through collaboration we support the engineers and scientists of tomorrow to become technically strong, analytically innovative and creative. Find out more about Science and Engineering at Manchester .

Programmes in related subject areas

Use the links below to view lists of programmes in related subject areas.

• Computer Science
• Informatics

Regulated by the Office for Students

The University of Manchester is regulated by the Office for Students (OfS). The OfS aims to help students succeed in Higher Education by ensuring they receive excellent information and guidance, get high quality education that prepares them for the future and by protecting their interests. More information can be found at the OfS website .

You can find regulations and policies relating to student life at The University of Manchester, including our Degree Regulations and Complaints Procedure, on our regulations website .

Computer Science (4 Year Programme) MPhil/PhD

London, Bloomsbury

The PhD programme in UCL Computer Science is a 4-year programme, in which you will work within research groups on important and challenging problems in the development of computer science. We have research groups that cover many of the leading-edge topics in computer science , and you will be supervised by academics at the very forefront of their field.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

• Entry requirements

A UK Master's degree in a relevant discipline with Merit, or a minimum of an upper second-class UK Bachelor's degree in a relevant discipline, or an overseas qualification of an equivalent standard. Work experience may also be taken into account.

The English language level for this programme is: Level 1

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

If you are intending to apply for a time-limited visa to complete your UCL studies (e.g., Student visa, Skilled worker visa, PBS dependant visa etc.) you may be required to obtain ATAS clearance . This will be confirmed to you if you obtain an offer of a place. Please note that ATAS processing times can take up to six months, so we recommend you consider these timelines when submitting your application to UCL.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

On this PhD programme, you will work within research groups on challenging computer science projects.

Our research groups cover leading-edge topics , and our academics are at the forefront of their field.

The research groups, the department , and the college, provide numerous opportunities to learn more about your field and the skills required to develop your research and future careers.

Who this course is for

This programme is best suited for people wishing to embark on an academic career, as well as those interested in finding work in industry. You will be assigned a first and second supervisor, who will guide you in the development of your research project and your abilities as a researcher. The research groups, the department, and the college, provide numerous opportunities for you to learn more about your field (e.g. seminars, conferences, and journal clubs) and the skills required for you to develop your research and future careers (e.g. training courses). Many of our students have had their research results published and recognised at leading international conferences during their time on the PhD programme.

What this course will give you

UCL is ranked 9th globally in the latest QS World University Rankings (2024), giving you an exciting opportunity to study at one of the world's best universities.

UCL Computer Science is recognised as a world leader in teaching and research. The department was ranked first in England and second in the UK for research power in Computer Science and Informatics in the most recent Research Excellence Framework ( REF2021 ). You will learn from leading experts with an outstanding reputation in the field. 

Code written at UCL is used across all 3G mobile networks for instant messaging and videoconferencing; medical image computing has led to faster prostate cancer diagnosis and has developed tools to help neurosurgeons avoid damaging essential communication pathways during brain surgery; and our human-centred approach to computer security has transformed the UK government's delivery of online security.

This MPhil/PhD in Computer Science is a research degree programme that will not only challenge and stimulate you, but also has the potential to lead to a varied and interesting career and introduce you to valuable contacts in academia and the industry.

The foundation of your career

Your employability will be greatly enhanced by working alongside world-leading researchers in cutting-edge research areas such as virtual environments, networked systems, human-computer interaction and financial computing. UCL's approach is multi-disciplinary and UCL Computer Science shares ideas and resources from across all departments of Faculty of Engineering Sciences and beyond. Our alumni have a successful record of finding work, or have founded their own successful start-up companies, because they have an excellent understanding of the current questions which face industry and have the skills and the experience to market innovative solutions.

Employability

UCL Computer Science graduates secure careers in a variety of organisations, including global IT consultancies, City banks and specialist companies in manufacturing industries.

The department takes pride in helping students in their career choices and offers placements and internships with numerous start-up technology companies, including those on Silicon Roundabout, world-leading companies such as Google, Skype and Facebook, and multi national finance companies, including Morgan Stanley, Deutsche Bank and JP Morgan.

Our graduates secure roles such as applications developers, information systems managers, IT consultants, multimedia programmers, software engineers and systems analysts in companies such as Microsoft, Cisco, Bloomberg, PwC and IBM.

UCL Computer Science is located in the heart of London and subsequently has strong links with industry. You will have regular opportunities to undertake internships at world-leading research organisations. We frequently welcome industry executives to observe your project presentations, and we host networking events with technology entrepreneurs.

You will also benefit from a location close to the City of London and Canary Wharf to work on projects with leading global financial companies. London is also home to numerous technology communities, for example the Graduate Developer Community, who meet regularly and provide mentors for students interested in finding developer roles when they graduate.

Teaching and learning

You are assigned a first and second supervisor who you will meet regularly. You are also assigned a research group who normally meet regularly for research seminars and related activities in the department.

You will participate in three vivas during the course of your study. These are useful feedback opportunities and allow you to demonstrate your understanding of the literature, your progress in your research and eventually, your final thesis and research. For each viva, you will be expected to produce a detailed report of your work to date and to attend a 'verbal exam' with supervisors and/or external academics/experts.

During your research degree, you will have regular meetings with your primary supervisor, in addition to contact with your secondary supervisor and participation in group meetings. Full-time study should comprise of 40 hours per week .

Research areas and structure

• Bioinformatics: protein structure; genome analysis; transmembrane protein modelling; de novo protein design methods; exploiting grid technology; mathematical modelling of biological processes
• Financial computing: software engineering; computational statistics and machine learning; mathematical modelling
• Human centred systems: usability of security and multimedia systems; making sense of information; human error and cognitive resilience
• Information security: human and organisational aspects of security; privacy-enhancing technologies; cryptography and cryptocurrencies; cybersecurity in public policy and international relations; systems security and cybercrime
• Intelligent systems: knowledge representation and reasoning; machine learning
• Media futures: digital rights management; information retrieval; computational social science; recommender systems
• Networks: internet architecture; protocols; mobile networked systems; applications and evolution; high-speed networking
• Programming Principles, Verification and Logic’: logic and the semantics of programs; automated tools for verification and program analysis; produce mathematically rigorous concepts and techniques that aid in the construction and analysis of computer systems; applied logic outreach in AI, security, biology, economics
• Software systems engineering: requirements engineering; software architecture; middleware technologies; distributed systems; software tools and environments; mobile computing
• Virtual environments: presence, virtual characters; interaction; rendering; mixed reality
• Vision and imaging science: face recognition; medical image analysis; statistical modelling of colour information; inverse problems and building mathematical models for augmented reality; diffusion tensor imaging

Research environment

UCL Computer Science is one of the leading university centres for computer science research in Europe. The department is very well-connected with research groups across the university, and is involved in many exciting multi-disciplinary research projects.

Furthermore, research groups in the department are heavily involved in collaborative research and development projects with other universities and with companies in the UK and internationally. UCL provides significant support for technology transfer, and in particular for technology start-ups, and the department has an increasingly successful record of spin-out companies including a number of spin-outs that have been acquired by Google, Facebook, Amazon, etc.

Month 0 Registration - initially MPhil registration.

Month 0-6 - General reading, directed by the supervisor, in the area of interest. This should bring you up to the sharp end of the area and allow you to appreciate what the research problems are.

Months 6-9 - More detailed reading, aimed at becoming expert enough to tackle a thesis project. A small focused project is in order here to pin the reading on. A report on the year's activities should begin to be prepared.

Month 9 - FORMAL 1ST-YEAR VIVA (10-12 for Part-time) This is the first major examination, and must take place no more than 9 months from the start date. A feedback activity. Given a read of your report, the supervisor, 2nd supervisor and an 'assessor' review the work done with the aim of providing you with proper feedback on your work. This is also a good opportunity to get feedback for the Transfer Viva and is often used as a “mock transfer”.

Months 12-18 - FORMAL TRANSFER VIVA (15-21 for Part-time) Also known as the “Upgrade Viva” - this is where you would upgrade your expected qualification from MPhil to PhD. A substantial project report is expected demonstrating the ability to conduct research, with initial research results, and a plan for completion of the work and writing of the thesis. The outcome of the viva will determine whether you are allowed to transfer registration from MPhil to PhD.

Months 24-36 - Thesis project work being tidied up and turned into a unified piece of work. Thesis writing being planned and chapters being drafted. You are now eligible for Completing Research Status

Month 36 - MOCK VIVA (48-60 for Part-time) A draft thesis and mock viva. This is to be attended by the supervisor, second supervisor and assessor and any others thought relevant. Thesis submission forms (aka Entry forms) completed and submitted.

Months 36-42 - Complete the writing of the thesis.

Month 42 - (60-72 for Part-time) Submit thesis.

See full-time summary

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

As each research project is unique in nature, the AFE (Additional Fee Element) is calculated on a student-by-student basis and is determined by your academic supervisor. Please contact your supervisor for further details.

A student conference and travel fund is available to students within the department to help with costs associated with attending and presenting at conferences. Applications are considered on a case-by-case basis.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

UCL offers various funding opportunities for postgraduate students. Please see UCL's Scholarships website for more information.

The department offers funding for overseas and UK students. Please see the Computer Science website for more information.

Home students will have the opportunity to apply for EPSRC DTP Studentships where available.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

CSC-UCL Joint Research Scholarship

Value: Fees, maintenance and travel (Duration of programme) Criteria Based on academic merit Eligibility: EU, Overseas

Deadlines and start dates are usually dictated by funding arrangements so check with the department or academic unit to see if you need to consider these in your application preparation. All applicants are asked to identify and contact potential supervisors before making an application. For more information see our How to apply page.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2024-2025

Got questions get in touch.

Computer Science

[email protected]

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PhD in Computer Science Topics 2023: Top Research Ideas

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If you want to embark on a  PhD  in  computer science , selecting the right  research topics  is crucial for your success. Choosing the appropriate  thesis topics  and research fields will determine the direction of your research. When selecting thesis topics for your research project, it is crucial to consider the compelling and relevant issues. The topic selection can greatly impact the success of your project in this field.

We’ll delve into various areas and subfields within  computer science research , exploring different projects, technologies, and ideas to help you narrow your options and find the perfect thesis topic. Whether you’re interested in  computer science research topics  like  artificial intelligence ,  data mining ,  cybersecurity , or any other  cutting-edge field  in computer science engineering, we’ve covered you with various research fields and analytics.

Stay tuned as we discuss how a well-chosen topic can shape your research proposal, journal paper writing process, thesis writing journey, and even individual chapters. We will address the topic selection issues and analyze how it can impact your communication with scholars. We’ll provide tips and insights to help research scholars and experts select high-quality topics that align with their interests and contribute to the advancement of knowledge in technology. These tips will be useful when submitting articles to a journal in the field of computer science.

Top PhD research topics in computer science for 2024

Exploration of Cutting-Edge Research Areas

As a Ph.D. student in computer science, you can delve into cutting-edge research areas such as technology, cybersecurity, and applications. These fields are shaping the future of deep learning and the overall evolution of computer science. One such computer science research field is  quantum computing , which explores the principles of quantum mechanics to develop powerful computational systems. It is an area that offers various computer science research topics and has applications in cybersecurity. By studying topics like quantum  algorithms  and quantum information theory, you can contribute to advancements in this exciting field. These advancements can be applied in various applications, including deep learning techniques. Moreover, your research in this area can also contribute to your thesis.

Another burgeoning research area is  artificial intelligence (AI) . With the rise of deep learning and the increasing integration of AI into various applications, there is a growing need for researchers who can push the boundaries of AI technology in cybersecurity and big data. As a PhD student specializing in AI, you can explore deep learning, natural language processing, and computer vision and conduct research in the field. These techniques have various applications and require thorough analysis. Your research could lead to breakthroughs in autonomous vehicles, healthcare diagnostics, robotics, applications, deep learning, cybersecurity, and the internet.

Discussion on Emerging Fields

In addition to established research areas, it’s important to consider emerging fields, such as deep learning, that hold great potential for innovation in applications and techniques for cybersecurity. One such field is cybersecurity. With the increasing number of cyber threats and attacks, experts in the cybersecurity field are needed to develop robust security measures for the privacy and protection of internet users. As a PhD researcher in cybersecurity, you can investigate topics like network security, cryptography, secure software development, applications, internet privacy, and thesis. Your work in the computer science research field could contribute to safeguarding sensitive data and protecting critical infrastructure by enhancing security and privacy in various applications.

Data mining is an exciting domain that offers ample opportunities for research in deep learning techniques and their analysis applications. With the rise of cloud computing, extracting valuable insights from vast amounts of data has become crucial across industries. Applications, research topics, and techniques in cloud computing are now essential for uncovering valuable insights from the data generated daily. By focusing your PhD studies on data mining techniques and algorithms, you can help organizations make informed decisions based on patterns and trends hidden within large datasets. This can have significant applications in privacy management and learning.

Bioinformatics is an emerging field that combines computer science with biology and genetics, with applications in big data, cloud computing, and thesis research. As a Ph.D. student in bioinformatics, you can leverage computational techniques and applications to analyze biological data sets and gain insights into complex biological processes. The thesis could focus on the use of cloud computing for these analyses. Your research paper could contribute to advancements in personalized medicine or genetic engineering applications. Your thesis could focus on learning and the potential applications of your findings.

Highlighting Interdisciplinary Topics

Computer science intersects with cloud computing, fog computing, big data, and various other disciplines, opening up avenues for interdisciplinary research. One such area is healthcare informatics, where computer scientists work alongside medical professionals to develop innovative solutions for healthcare challenges using cloud computing and fog computing. The collaboration involves the management of these technologies to enhance healthcare outcomes. As a PhD researcher in healthcare informatics, you can explore electronic health records, medical imaging analysis, telemedicine, security, learning, management, and cloud computing. Your work in healthcare management could profoundly impact improving patient care and streamlining healthcare systems, especially with the growing importance of learning and implementing IoT technology while ensuring security.

Computational social sciences is an interdisciplinary field that combines computer science with social science methodologies, including cloud computing, fog computing, edge computing, and learning. Studying topics like social networks or sentiment analysis can give you insights into human behavior and societal dynamics. This learning can be applied to mobile ad hoc networks (MANETs) security management. Your research on learning, security, cloud computing, and IoT could contribute to understanding and addressing complex social issues such as online misinformation or spreading infectious diseases through social networks.

Guidance on selecting thesis topics for computer science PhD scholars

Importance of aligning personal interests with current trends and gaps in existing knowledge.

Choosing a thesis topic is an important decision for  computer science PhD scholars , especially in IoT. It is essential to consider topics related to learning, security, and management to ensure a well-rounded research project. It is essential to align personal interests with current trends in learning, management, security, and IoT and fill gaps in existing knowledge. By choosing a learning topic that sparks your passion for management, you are more likely to stay motivated throughout the research process on the cutting edge of IoT. Aligning your interests with the latest advancements in cloud computing and fog computing ensures that your work in computer science contributes to the field’s growth. Additionally, staying updated on the latest developments in learning and management is essential for your professional development.

Conducting thorough literature reviews is vital to identify potential research gaps in the field of learning management and security. Additionally, it is important to consider the edge cases and scenarios that may arise. Dive into relevant academic journals, conferences, and publications to understand current research in learning management, security, and mobile. Look for areas with limited studies or conflicting findings in security, fog, learning, and management, indicating potential gaps that need further exploration. By identifying these learning and management gaps, you can contribute new insights and expand the existing knowledge on security and fog.

Tips on Conducting Thorough Literature Reviews to Identify Potential Research Gaps

When conducting literature reviews on mobile learning management, it is important to be systematic and comprehensive while considering security. Here are some tips for effective mobile security management and learning. These tips will help you navigate this process effectively.

• Start by defining specific keywords related to your research area, such as security, learning, mobile, and edge, and use them when searching for relevant articles.
• Utilize academic databases like IEEE Xplore, ACM Digital Library, and Google Scholar for comprehensive cloud computing, edge computing, security, and machine learning coverage.
• Read abstracts and introductions of articles on learning, security, blockchain, and cloud computing to determine their relevance before diving deeper into full papers.
• Take notes while learning about security in cloud computing to keep track of key findings, methodologies used, and potential research gaps.
• Look for recurring themes or patterns in different studies related to learning, security, and cloud computing that could indicate areas needing further investigation.

By following these steps, you can clearly understand the existing literature landscape in the fields of learning, security, and cloud computing and identify potential research gaps.

Consideration of Practicality, Feasibility, and Available Resources When Choosing a Thesis Topic

While aligning personal interests with research trends in security, learning, and cloud computing is crucial, it is equally important to consider the practicality, feasibility, and available resources when choosing a thesis topic. Here are some factors to keep in mind:

• Practicality: Ensure that your research topic on learning cloud computing can be realistically pursued within your PhD program’s given timeframe and scope.
• Feasibility: Assess the availability of necessary data, equipment, software, or other resources required for learning and conducting research effectively on cloud computing.
• Consider whether there are learning opportunities for collaboration with industry partners or other researchers in cloud computing.
• Learning Cloud Computing Advisor Expertise: Seek guidance from your advisor who may have expertise in specific areas of learning cloud computing and can provide valuable insights on feasible research topics.

Considering these factors, you can select a thesis topic that aligns with your interests and allows for practical implementation and fruitful collaboration in learning and cloud computing.

Identifying good research topics for a Ph.D. in computer science

Strategies for brainstorming unique ideas

Thinking outside the box and developing unique ideas is crucial when learning about cloud computing. One effective strategy for learning cloud computing is to leverage your personal experiences and expertise. Consider the challenges you’ve faced or the gaps you’ve noticed in your field of interest, especially in learning and cloud computing. These innovative research topics can be a starting point for learning about cloud computing.

Another approach is to stay updated with current trends and advancements in computer science, specifically in cloud computing and learning. By focusing on  emerging technologies  like cloud computing, you can identify areas ripe for exploration and learning. For example, topics related to artificial intelligence, machine learning, cybersecurity, data science, and cloud computing are highly sought after in today’s digital landscape.

Importance of considering societal impact and relevance

While brainstorming research topics, it’s crucial to consider the societal impact and relevance of your work in learning and cloud computing. Think about how your research in cloud computing can contribute to learning and solving real-world problems or improving existing systems. This will enhance your learning in cloud computing and increase its potential for funding and collaboration opportunities.

For instance, if you’re interested in learning about cloud computing and developing algorithms for autonomous vehicles, consider how this technology can enhance road safety, reduce traffic congestion, and improve overall learning. By addressing pressing issues in the field of learning and cloud computing, you’ll be able to contribute significantly to society through your research.

Seeking guidance from mentors and experts

Choosing the right research topic in computer science can be overwhelming, especially with the countless possibilities within cloud computing. That’s why seeking guidance from mentors, professors, or industry experts in computing and cloud is invaluable.

Reach out to faculty members who specialize in your area of interest in computing and discuss potential research avenues in cloud computing with them. They can provide valuable insights into current computing and cloud trends and help you refine your ideas based on their expertise. Attending computing conferences or cloud networking events allows you to connect with professionals with firsthand knowledge of cutting-edge research areas in computing and cloud.

Remember that feedback from experienced individuals in the computing and cloud industry can help you identify your chosen research topic’s feasibility and potential impact.

Tools and simulation in computer science research

Overview of popular tools for simulations, modeling, and experimentation.

In computing and cloud, utilizing appropriate tools and simulations is crucial for conducting effective studies in computer science research. These computing tools enable researchers to model and experiment with complex systems in the cloud without the risks associated with real-world implementation. Valuable insights can be gained by simulating various scenarios in cloud computing and analyzing the outcomes.

MATLAB is a widely used tool in computer science research, which is particularly valuable for computing and working in the cloud. This software provides a range of functions and libraries that facilitate numerical computing, data visualization, and algorithm development in the cloud. Researchers often employ MATLAB for computing to simulate and analyze different aspects of computer systems, such as network performance or algorithm efficiency in the cloud. Its versatility makes computing a popular choice across various domains within computer science, including cloud computing.

Python libraries also play a significant role in simulation-based studies in computing. These libraries are widely used to leverage the power of cloud computing for conducting simulations. Python’s extensive collection of libraries offers researchers access to powerful tools for data analysis, machine learning, scientific computing, and cloud computing. With libraries like NumPy, Pandas, and TensorFlow, researchers can develop sophisticated models and algorithms for computing in the cloud to explore complex phenomena.

Network simulators are essential in computer science research, specifically in computing. These simulators help researchers study and analyze network behavior in a controlled environment, enabling them to make informed decisions and advancements in cloud computing. These computing simulators allow researchers to study communication networks in the cloud by creating virtual environments to evaluate network protocols, routing algorithms, or congestion control mechanisms. Examples of popular network simulators in computing include NS-3 (Network Simulator 3) and OMNeT++ (Objective Modular Network Testbed in C++). These simulators are widely used for testing and analyzing various network scenarios, making them essential tools for researchers and developers working in the cloud computing industry.

The Benefits of Simulation-Based Studies

Simulation-based studies in computing offer several advantages over real-world implementations when exploring complex systems in the cloud.

• Cost-Effectiveness: Conducting large-scale computing experiments in the cloud can be prohibitively expensive due to resource requirements or potential risks. Simulations in cloud computing provide a cost-effective alternative that allows researchers to explore various scenarios without significant financial burdens.
• Cloud computing provides a controlled environment where researchers can conduct simulations. These simulations enable them to manipulate variables precisely within the cloud. This level of control in computing enables them to isolate specific factors and study their impact on the cloud system under investigation.
• Rapid Iteration: Simulations in cloud computing enable researchers to iterate quickly, making adjustments and refinements to their models without the need for time-consuming physical modifications. This agility facilitates faster progress in  research projects .
• Scalability: Computing simulations can be easily scaled up or down in the cloud to accommodate different scenarios. Researchers can simulate large-scale computing systems in the cloud that may not be feasible or practical to implement in real-world settings.

Application of Simulation Tools in Different Domains

Simulation tools are widely used in various domains of computer science research, including computing and cloud.

• In robotics, simulation-based studies in computing allow researchers to test algorithms and control strategies before deploying them on physical robots. The cloud is also utilized for these simulations. This approach helps minimize risks and optimize performance.
• For studying complex systems like traffic flow or urban planning, simulations in computing provide insights into potential bottlenecks, congestion patterns, or the effects of policy changes without disrupting real-world traffic. These simulations can be run using cloud computing, which allows for efficient processing and analysis of large amounts of data.
• In computing, simulations are used in machine learning and artificial intelligence to train reinforcement learning agents in the cloud. These simulations create virtual environments where the agents can learn from interactions with simulated objects or environments.

By leveraging simulation tools like MATLAB and Python libraries, computer science researchers can gain valuable insights into complex computing systems while minimizing costs and risks associated with real-world implementations. Using network simulators further enhances their ability to explore and analyze cloud computing environments.

Notable algorithms in computer science for research projects

Choosing the right research topic is crucial. One area that offers a plethora of possibilities in computing is algorithms. Algorithms play a crucial role in cloud computing.

PageRank: Revolutionizing Web Search

One influential algorithm that has revolutionized web search in computing is PageRank, now widely used in the cloud. Developed by Larry Page and Sergey Brin at Google, PageRank assigns a numerical weight to each webpage based on the number and quality of other pages linking to it in the context of computing. This algorithm has revolutionized how search engines rank webpages, ensuring that the most relevant and authoritative content appears at the top of search results. With the advent of cloud computing, PageRank has become even more powerful, as it can now analyze vast amounts of data and provide accurate rankings in real time. This algorithm played a pivotal role in the success of Google’s computing and cloud-based search engine by providing more accurate and relevant search results.

Dijkstra’s Algorithm: Finding the Shortest Path

Another important algorithm in computer science is Dijkstra’s algorithm. Named after its creator, Edsger W. Dijkstra, this computing algorithm efficiently finds the shortest path between two nodes in a graph using cloud technology. It has applications in various fields, such as network routing protocols, transportation planning, cloud computing, and DNA sequencing.

RSA Encryption Scheme: Securing Data Transmission

In computing, the RSA encryption scheme is one of the most widely used algorithms in cloud data security. Developed by Ron Rivest, Adi Shamir, and Leonard Adleman, this asymmetric encryption algorithm ensures secure communication over an insecure network in computing and cloud. Its ability to encrypt data using one key and decrypt it using another key makes it ideal for the secure transmission of sensitive information in the cloud.

Recent Advancements and Variations

While these computing algorithms have already left an indelible mark on  computer science research projects , recent advancements and variations continue expanding their potential cloud applications.

• With the advent of  machine learning techniques  in computing, algorithms like support vector machines (SVM), random forests, and deep learning architectures have gained prominence in solving complex problems involving pattern recognition, classification, and regression in the cloud.
• Evolutionary Algorithms: Inspired by natural evolution, evolutionary algorithms such as genetic algorithms and particle swarm optimization have found applications in computing, optimization problems, artificial intelligence, data mining, and cloud computing.

Exploring emerging trends: Big data analytics, IoT, and machine learning

The computing and computer science field is constantly evolving, with new trends and technologies in cloud computing emerging regularly.

Importance of Big Data Analytics

Big data refers to vast amounts of structured and unstructured information that cannot be easily processed using traditional computing methods. With the rise of cloud computing, handling and analyzing big data has become more efficient and accessible. Big data analytics in computing involves extracting valuable insights from these massive datasets in the cloud to drive informed decision-making.

With the exponential growth in data generation across various industries, big data analytics in computing has become increasingly important in the cloud. Computing enables businesses to identify patterns, trends, and correlations in the cloud, leading to improved operational efficiency, enhanced customer experiences, and better strategic planning.

One significant application of big data analytics is in computing research in the cloud. By analyzing large datasets through advanced techniques such as data mining and predictive modeling in computing, researchers can uncover hidden patterns or relationships in the cloud that were previously unknown. This allows for more accurate predictions and a deeper understanding of complex phenomena in computing, particularly in cloud computing.

The Potential Impact of IoT

The Internet of Things (IoT) refers to a network of interconnected devices embedded with sensors and software that enable them to collect and exchange data in the computing and cloud fields. This computing technology has the potential to revolutionize various industries by enabling real-time monitoring, automation, and intelligent decision-making in the cloud.

Computer science research topics in computing, including IoT and cloud computing, open up exciting possibilities. For instance, sensor networks can be deployed for environmental monitoring or intrusion detection systems in computing. Businesses can leverage IoT technologies for optimizing supply chains or improving business processes through increased connectivity in computing.

Moreover, IoT plays a crucial role in industrial computing settings, facilitating efficient asset management through predictive maintenance based on real-time sensor readings. Biometrics applications in computing benefit from IoT-enabled devices that provide seamless integration between physical access control systems and user authentication mechanisms.

Enhancing Decision-Making with Machine Learning

Machine learning techniques are leading the way in technological advancements in computing. They involve computing algorithms that enable systems to learn and improve from experience without being explicitly programmed automatically. Machine learning is a branch of computing with numerous applications, including natural language processing, image recognition, and data analysis.

In research projects, machine learning methods in computing can enhance decision-making processes by analyzing large volumes of data quickly and accurately. For example, deep learning algorithms in computing can be used for sentiment analysis of social media data or for predicting disease outbreaks based on healthcare records.

Machine learning also plays a vital role in automation. Autonomous vehicles heavily depend on machine learning models for computing sensor data and executing real-time decisions. Similarly, industries can leverage machine learning techniques in computing to automate repetitive tasks or optimize complex business processes.

The future of computer science research

We discussed the top PhD research topics in computing for 2024, provided guidance on selecting computing thesis topics, and identified good computing research areas. Our research delved into the tools and simulations utilized in computing research. We specifically focused on notable algorithms for computing research projects. Lastly, we touched upon emerging trends in computing, such as big data analytics, the Internet of Things (IoT), and machine learning.

As you embark on your journey to pursue a PhD in computing, remember that the field of computer science is constantly evolving. Stay curious about computing, embrace new computing technologies and methodologies, and be open to interdisciplinary collaborations in computing. The future of computing holds immense potential for groundbreaking discoveries that can shape our world.

If you’re ready to dive deeper into the world of computing research or have any questions about specific computing topics, don’t hesitate to reach out to experts in the computing field or join relevant computing communities where computing ideas are shared freely. Remember, your contribution to computing has the power to revolutionize technology and make a lasting impact.

What are some popular career opportunities after completing a PhD in computer science?

After completing a PhD in computer science, you can explore various career paths in computing. Some popular options in the field of computing include becoming a university professor or researcher, working at renowned tech companies as a senior scientist or engineer, pursuing entrepreneurship by starting your own tech company or joining government agencies focusing on cutting-edge technology development.

How long does it typically take to complete a PhD in computer science?

The duration of a Ph.D. program in computing varies depending on factors such as individual progress and program requirements. On average, it takes around four to five years to complete a full-time computer science PhD specializing in computing. However, part-time options may extend the duration.

Can I specialize in multiple areas within computer science during my PhD?

Yes! Many computing programs allow students to specialize in multiple areas within computer science. This flexibility in computing enables you to explore diverse research interests and gain expertise in different subfields. Consult with your academic advisor to plan your computing specialization accordingly.

How can I stay updated with the latest advancements in computer science research?

To stay updated with the latest advancements in computing, consider subscribing to relevant computing journals, attending computing conferences and workshops, joining online computing communities and forums, following influential computing researchers on social media platforms, and participating in computing research collaborations. Engaging with the vibrant computer science community will inform you about cutting-edge computing developments.

Are there any scholarships or funding opportunities available for PhD students in computer science?

Yes, numerous scholarships and funding opportunities are available for  PhD students  in computing. These computing grants include government agency grants, university or research institution fellowships, industry-sponsored computing scholarships, and international computing scholarship programs. Research thoroughly to find suitable options that align with your research interests and financial needs.

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PhD Topics in Computer Science for Real-World Applications

Welcome to the fascinating world of PhD topics in computer science , where innovation, intellect, and real-world applications converge to pave the way for groundbreaking research. In this world of limitless possibilities, computer science PhD topics offer an unparalleled opportunity for aspiring researchers to delve into cutting-edge domains, unleashing their creativity to address the pressing challenges of our time. Embark on a journey of intellectual exploration as we uncover the most captivating and relevant computer science topics for PhD research, guiding you towards shaping the future through your passion for technology and its transformative potential. 

Some Specific Examples of Computer Science Topics For PhD Research That Have Real-World Applications

1 . AI-Powered Healthcare Diagnostics:

Computer science plays a critical role in advancing healthcare diagnostics through artificial intelligence (AI). By leveraging machine learning and deep learning algorithms, researchers can develop systems capable of accurately diagnosing medical conditions from various sources such as medical imaging, patient records, and genetic data. A potential PhD topic in this field could focus on:

- Deep Learning for Medical Image Analysis: Develop advanced convolutional neural networks (CNNs) or other deep learning models to automatically analyze medical images like X-rays, MRIs, or CT scans. The aim is to detect and classify abnormalities, enabling early detection and precise diagnosis.

- Predictive Analytics for Personalized Medicine: Utilize AI techniques to analyze patient data and identify patterns that can lead to personalized treatment plans. By integrating genetic information, medical history, and lifestyle data, the research can help tailor treatments to individual patients, optimizing outcomes.

2. Sustainable Smart Cities:

Computer science offers innovative solutions for creating energy-efficient and sustainable smart cities, integrating information technology with urban infrastructure. A PhD research topic in this domain could explore:

- IoT-Based Resource Management: Design and implement Internet of Things (IoT) solutions to monitor and manage resource consumption in cities, such as energy, water, and waste. Develop algorithms that optimize resource allocation and reduce environmental impact.

- Smart Transportation Systems: Propose intelligent transportation systems that use real-time data, including traffic patterns, public transport usage, and weather conditions, to optimize commuting and reduce congestion, thereby lowering carbon emissions.

3. Cybersecurity for Critical Infrastructures :

With the growing dependence on digital systems, securing critical infrastructures is of paramount importance. A PhD research topic in this field can focus on:

- Threat Detection and Response: Develop AI-driven cybersecurity solutions that use machine learning algorithms to detect and respond to cyber threats in real-time, enhancing the resilience of critical infrastructure systems.

- Blockchain-Based Security for Critical Systems: Investigate the applications of blockchain technology in securing critical infrastructure, such as ensuring the integrity of data and facilitating secure communication between components.

4. Autonomous Systems for Disaster Response:

Autonomous systems can significantly improve disaster response efforts, reducing the risks to human responders and enhancing the speed and effectiveness of rescue missions. A potential PhD topic in this area could be:

- Swarm Robotics for Disaster Response: Explore swarm robotics, where a large number of small robots collaborate to execute search and rescue missions in disaster-stricken areas. Develop algorithms for coordination, path planning, and communication among the robots.

- Real-Time Environmental Sensing with Drones: Investigate the use of drones equipped with sensors to collect real-time data on disaster-affected regions. Develop AI-powered algorithms to analyze this data and aid in decision-making during disaster response operations.

5. Natural Language Processing for Multilingual Communication :

Breaking down language barriers through natural language processing (NLP) can have significant societal and economic impacts. A PhD topic in this area could focus on:

- Cross-Lingual Information Retrieval: Develop NLP algorithms that enable users to search for information in one language and retrieve relevant results from documents in multiple languages, fostering global information access.

- Multilingual Sentiment Analysis: Explore sentiment analysis techniques that can accurately determine emotions and opinions expressed in text across different languages. This research can find applications in brand monitoring, customer feedback analysis, and social media sentiment tracking.

Identifying a Research Topic That Aligns With Both Researchers’ Interests and the Current Needs of Industries

1. Self-Reflection and Passion Discovery: Begin by delving deep into your own interests and strengths within computer science. What excites you the most? What problems ignite your curiosity? Identifying your true passions will pave the way for a research topic that you can wholeheartedly dedicate yourself to.

2. Stay Abreast of Industry Trends: Immerse yourself in the dynamic landscape of computer science industries. Follow the latest advancements, read research papers, and attend conferences to understand the pressing challenges faced by technology-driven sectors. Engaging with industry experts and professionals can provide valuable insights into potential research gaps.

3. Dialogue with Academic Mentors: Seek guidance from experienced academics or mentors in the field of computer science. They can help you refine your research interests and align them with the current needs of industries and society. Discussions with experts can unearth potential avenues for impactful research.

4. Collaborate and Network: Engage in interdisciplinary collaborations with researchers from diverse fields. This can open up new perspectives and reveal exciting intersections between your interests and real-world challenges. Attend workshops and seminars to expand your network and gain fresh ideas.

5. Literature Review and Gap Analysis: Conduct a thorough literature review to understand the existing body of knowledge in your chosen area. Identify gaps where your expertise can contribute to solving practical problems. Building upon existing research ensures your work remains relevant and impactful.

At PhD Box, we understand that identifying a research topic that perfectly aligns with your passions and addresses real-world needs is crucial for a fulfilling PhD journey. Our program is designed to support you in this exhilarating quest by providing personalized assistance throughout the process. Through tailored guidance from experienced academics and industry experts, we help you explore your interests, refine your research goals, and identify the most relevant and impactful topics. At PhD Box, we are dedicated to empowering you to embark on a transformative PhD journey, where your passion and expertise converge to create tangible real-world solutions that make a positive and lasting impact.

Striking a Balance Between Theoretical Rigor and Practical Implementation in the Chosen PhD Topic

1. Strong Theoretical Foundation: Lay a sturdy groundwork by thoroughly understanding the theoretical underpinnings of your chosen PhD topic. Immerse yourself in existing literature, grasp fundamental concepts, and study relevant methodologies. A robust theoretical foundation is the bedrock of innovative and impactful research.

2. Identify Real-World Challenges: Ground your research in real-world challenges faced by industries, communities, or societal domains. Strive to comprehend the practical implications of your work and align it with the needs of those who can benefit from your contributions.

3. Formulate Concrete Objectives: Define clear and achievable research objectives that bridge the gap between theory and practice. Outline tangible goals and outcomes that showcase the potential for real-world application and address specific issues.

4. Iterative Prototyping and Testing: Embrace the iterative nature of research. Develop prototypes and practical implementations to validate your theoretical findings. Rigorously test your solutions in simulated or real-world scenarios to ensure their practicality and effectiveness.

5. Engage with End-Users: Collaborate with end-users, industry professionals, or stakeholders who can provide valuable feedback on your research. Involving them from the early stages can offer insights into practical challenges and improve the applicability of your work.

At PhD Box, we recognize the significance of striking a harmonious balance between theoretical rigour and practical implementation in your chosen computer science PhD topic. Our program is tailored to equip you with the tools and support needed to achieve this delicate balance successfully. Through our expert guidance, you can develop a strong theoretical foundation, ensuring that your research is built on solid academic principles. Our cutting-edge resources empower you to prototype and test your solutions, bridging the gap between theory and real-world applicability. At PhD Box, we are committed to nurturing your research journey, empowering you to navigate the complexities of theoretical and practical aspects seamlessly. Let us be your trusted ally in crafting a PhD endeavour that not only showcases theoretical excellence but also translates into tangible, relevant, and impactful contributions in real-world settings.

Final Thoughts

Pursuing a PhD in computer science offers an exhilarating journey of innovation and research, where interdisciplinary collaboration, staying informed about current trends, and focusing on real-world applications play crucial roles. While the process of finding the right topic may be challenging, grounding research in a strong theoretical foundation and identifying gaps in existing literature can aid in narrowing down suitable directions. By embracing determination, dedication, and a passion for making a meaningful difference, computer scientists can leave an indelible mark on the world, contributing to the ever-evolving landscape of technology and addressing pressing global challenges. Let us embark together on this remarkable quest to shape the future of computer science.

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Department of Computer Science

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PhD in Computer Science

You will be based in the Department of Computer Science overlooking the lake on Campus East .

You will benefit from modern offices and collaboration spaces, and well-equipped research labs with a specialist in-department team to support your requirements throughout your studies. 

We will provide you with a laptop connected to the University network, and you will have 24/7 access to your desk and workspace. Distance learning students are allocated a work desk for the duration of their stay while they are in York.

For on-campus researchers, most of your training and supervision meetings will take place on campus at the University of York, though your research may take you further afield.

PhD by distance learning

We offer the opportunity to study for a PhD by distance learning. This is available to students based in the UK and abroad, studying full-time or part-time. Our PhD by distance learning offers the same high quality of supervisory support (primarily online), and demands the same level of academic rigour as a campus-based PhD.

You will undertake your research and thesis production remotely, joining us on campus only occasionally. You will be expected to visit York at your own expense at the following stages of your study:

• Two weeks at the start of enrolment for induction, to meet your supervisor and your research group, and to meet other PhD students;
• Two one-week visits each year at important stages ('milestones') of your study (the number of visits is reduced accordingly if you are a part-time student);
• You will normally attend your PhD viva in person.

When you are not in York, you will continue to benefit from regular supervision meetings using online communication platforms, such as Zoom. Read more about how we support distance learners .

Are you an international applicant? It is important for you to note that it is your responsibility to meet any requirements for legal entry into the UK at the time of each of your visits. While the University and Department can provide supporting letters, the University cannot make any guarantees regarding entry visas or legal residence.  Read more about applying for a visa.

Entry requirements

Undergraduate and masters degrees.

The PhD in Computer Science is intended for students who already have a good first degree in Computer Science or a related field.

For entry to the PhD programme, we require at least a 2:1 undergraduate degree, or a qualification equivalent to a UK Masters degree with a minimum average grade of 60%.

We are willing to consider your application if you do not fit this profile, providing you are able to demonstrate that you have the required amount of Computer Science knowledge and experience to succeed on the programme.

English language requirements

If English is not your first language you must provide evidence of your ability.

Find out more about English Language requirements for research degrees

How to apply

Find a potential supervisor.

You should find a potential supervisor in our Department whose area of research overlaps with yours. We encourage you to contact them to discuss your research proposal before you apply. Please identify the name of your potential supervisor in your application.

On our Research web pages, you can explore our research groups which reflect the core research strengths and expertise within the Department of Computer Science. On the web page for each research group, you'll find more information about the aims and objectives of the group and the names of group members. You can use this information to identify the groups where research interests match your own.

If you have any questions or need further information, please contact [email protected] .

Submit your application

We require you to submit the following documents:

• Research proposal
• Academic transcript(s )
• Your curriculum vitae (CV)
• Personal statement
• Details of two academic referees

Your research proposal needs to outline the nature of your proposed study and give some indication of how you will conduct your research. The purpose of this exercise is to ensure that you and your potential supervisor(s) have matching research interests.

Your proposal can build on your chosen supervisor's area of work and may be prepared with the help of your chosen supervisor. It should be about 500 to 1,000 words in length, in English and in your own words. Read more about writing a research proposal .

You can apply and send all your documentation electronically through our online system. You don’t need to complete your application all at once: you can start it, save it and finish it later.

After you have applied, you can track the status of your application and view any official correspondence online. If you have applied for an advertised scholarship, decisions on funded places may take a little longer.

Applicant interviews

If we are impressed by your full application, personal statement and references, we will invite you to interview.

The interview panel will be made up of your potential supervisor(s) and another independent academic. During your interview, it is important that you demonstrate an understanding of your chosen topic and its supporting theories.

For students based outside the UK, interviews are held online via Zoom. Applicants based in the UK are offered the opportunity to attend their interview in York. If you choose to attend in person, your visit will include a tour of the Department and its facilities.

Related links Explore our PhD opportunities Research groups in the Department of Computer Science About our research degrees Applying for a research degree Funding for research degrees Information for International students Accommodation Life at York

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Latest PhD Topics in Computer Science

Computer science is denoted as the study based on computer technology about both the software and hardware. In addition, computer science includes various fields with the fundamental skills that are appropriate and that are functional over the recent technologies and the interconnected world. We guide research scholars to design latest phd topics in computer science.

Introduction to Computer Science

In general, the computer science field is categorized into a range of sub-disciplines and developed disciplines . The computer science field has the extension of some notable areas such as.

• Scientific computing
• Software system
• Hardware system
• Computer Theory

We have an updated technical team to provide novel research ideas with the appropriate theorems, proofs, source code, and data about tools. So, the research scholars can communicate with our research experts in computer science for your requirements. Now, let us discuss the significant research areas that are used to select the latest PhD topics in computer science in the following.

Research Area in Computer Science

• Internet-based mobile ad hoc network (iMANET)
• Smartphone ad hoc network (SPANET)
• Mobile cloud computing
• Soft computing
• Context-aware computing
• Systems and cybernetics
• Learning technologies
• Internet computing
• Information forensics and security
• Dependable and secure computing
• Brain-computer interface
• Audio and language processing
• Wireless sensor networks
• Wireless body area network
• Visual cryptography
• Video streaming
• Vehicular network
• Ad hoc network
• Text mining
• Telecommunication engineering
• Software-defined networking
• Software reengineering
• Service computing (web service)
• Social sensor networks
• Network security and routing
• Cloud computing
• Computer vision and image processing
• Bioinformatics and biotechnology
• Big data and databases
• Cyber security
• Natural language processing
• Embedded systems
• Human-computer interaction
• Networks and security

Frequently, all the research areas in computer science are quite innovative. In addition, we focus on innovative computer science projects and examine all the sections of research works through the models, techniques, algorithms, mechanisms , etc. Now, it’s time to pay equal attention to the consequence of research protocols. So, let us take a glance over the notable protocols that are used in computer science-based projects along with their specifications.

Protocols in Computer Science

• Ad hoc on-demand distance vector is abbreviated as AODV and it is based on the loop-free routing protocol for the ad hoc networks. It is created for the self-starting environment with the mobile nodes along with various network features that include packet loss, link failure, and node mobility
• It is denoted as the reactive and proactive routing protocol in which the routes are revealed as per the necessity
• Dynamic source routing abbreviated as DSR is one of the routing protocols that is used for the functions of wireless mesh networks and it is parallel to the AODV in transmitting the node requests

The above-mentioned are the substantial research protocols along with their descriptions . Thus, you can just contact us to get the finest and latest PhD topics in computer science. Our research experts can help you in all aspects of your research. Now, you can refer to the following to know about the research trends in computer science.

Current Trends in Computer Science

• It is deployed in the process of detecting and segregating the zombie attack based on cloud computing
• Stenography technique is applied in the cloud computing process to develop the security in cloud data
• In the network process, the reduction of fault occurs through the enhancement of green cloud computing
• In cloud computing, the issues are based on load balancing through the usage of a weight-based scheme
• Homomorphic encryption is developed for key sharing and management
• It is deployed in the cloud computing to segregate the virtual side-channel attack
• It is used to develop the cloud data security and watermarking technique in the cloud computing

The following is about the guidelines for research scholars to prepare the finest research work provided by our experienced research professionals.

How to do Good Research in Computer Science?

• Initially, select the research area that you are interested in computer science
• After selecting an area, the researcher has to find an innovative research topic in computer science
• Select good ideas to enhance the state of art
• The real-time implementations are applied
• Possessions based on the selected approach have to be proved and that should be the enhancement of the existing process
• Software tools have to be developed to support the system
• Have to describe the systematic comparison with the other approaches which has the same issue and discuss the advantages and disadvantages of the research notion
• Results based on some research papers have to be accessible

Applications in Computer Science

Manet is deployed to identify some applications in the research areas that are highlighted in the following.

• Detecting the selective forwarding attack in the mobile as hoc networks
• Avoidance of congestion in the mobile ad hoc networks
• It is used in the trust and security-based mechanism of wormhole attack isolation based on Manet
• Scheme is evaluated with the recovery of mobile as hoc network
• Road safety
• Vehicular ad hoc communication
• Environment sensors

The following is the list of research applications in the field of image processing .

• Video processing
• Pattern recognition
• Color processing
• Robot vision
• Encoding and transmission
• Medical field
• Gamma-rayay imaging

In addition, we have highlighted some applications that are related to the bioinformatics research field.

• Modeling and simulation based on proteins, RNA, and DNA are created through tools based on bioinformatics
• It is used to compare the genetic data along with the assistance of bioinformatics tools
• It is deployed in the study of various aspects including protein regulation and expression
• Organization of biological data and text mining has a significant phase in the process
• It is used in the field of genetics for the mutation observation

More than above, the utmost research applications are available in real-time. In overall, it increases the inclusive efficiency in all aspects of the research features. In addition, our research experts have listed down the prominent research topics based on computer science.

• Network and security
• Distributed system
• High-performance computing
• Visualization and graphics
• Geographical information system
• Databases and data mining
• Architectures and compiler optimization

List of Few Latest and Trending Research Topics in Big Data

• The parallel multi-classification algorithm for big data using the extreme learning machine
• Disease prediction through machine learning through big data from the healthcare communities
• Nearest neighbor classification for high-speed big data streams using spark
• Privacy preserving big data publishing: A scalable k-anonymization approach using MapReduce
• Efficient and rapid machine learning algorithms for big data and dynamic varying systems

Software Engineering-Based Topics in Computer Science

• It is used to support team awareness and collaboration, distributed software development, open source communities, and software as the service
• Software modeling and reasoning
• The reasoning and modeling based on software along with the reasoning specifications in security and safety, analysis of model-driven software development, analysis of requirements modifications, and product timeline
• Dependencies of stakeholders
• Enterprise contexts
• Modeling and analysis of software requirements

Latest Computer Networking Topics for Research

• Data security in the local network through the distributed firewalls
• Efficient peer-to-peer keyword searching
• Tolerant routing on mobile ad hoc network
• Hybrid global-local indexing for efficient peer-to-peer information retrieval
• Application of genetic algorithms in network routing
• Bluetooth-based smart sensor networks
• ISO layering model
• Distributed processing and networks
• Delay tolerant network
• Wireless intelligent networking
• Network security and cryptography

The abovementioned are the contemporary and topical research topics based on the computer science research field. In addition, the research experts have highlighted the latest phd topics in computer science domain detailed in the following.

Area-Based Topics Process

• Human-robot interaction
• Digital fabrication
• Critical computing
• UI technologies
• Information visualization
• Information and communication technology and development (ICTD)
• Computer-supported cooperative work
• Computer-supported cooperative learning
• Augmented and virtual reality
• Shape modeling
• Geometry processing
• Computational imaging
• Computing fabrication
• Translating computational tools
• NLP and speech for healthcare and medicine
• Satisfiability in reasoning
• Sequential decision making
• Multi-agentnt system
• Cognitive robotics
• Knowledge representation
• Human motion analysis
• Computational photography
• Object recognition
• Physics-based modeling of shape and appearance
• Cognitive modeling of language acquisition and processing
• Applications of NLP in healthcare and medicine
• Formal perspectives on language
• Applications of NLP in social sciences and humanities
• Machine translation
• Speech processing

Now, let’s have a glance over the list of research tools that are used in the implementation of research in computer science.

Simulation Tools in Computer Science

For your information, our technical professionals from computer science backgrounds have given you some foremost research questions with answers, to what the researchers are looking for.

Research Questions Computer Science

How to implement ad hoc routing protocols using omnet++.

Oment++ environment is implemented through the adaptations and it is enabling for the contrast simulation results with the designs of the Manet application. The routing protocols such as DSR and AODV are used in the process and as the open source code.

How is Hadoop used in big data?

In general, Hadoop is considered as the java and open source framework that is deployed in the process of big data storing. Mapreduce programming model is deployed in Hadoop for the speed process of data storage.

What are the trending technologies in computer science?

• Artificial intelligence (AI)
• Everything as a service
• Human augmentation
• Big data analytics
• Intelligent process automation (IPA)
• Internet of behaviors (IoB)
• 5G technology

What are the major areas in the field of computer science?

• Theory of computing
• Bioinformatics
• Software engineering
• Programming languages
• Numerical analysis
• Vision and Graphics
• Human-computerer interaction
• Database systems
• Computer systems and network security

How to implement artificial intelligence in python?

Generally, this process includes four significant steps and they are highlighted in the following.

• Organizational and AI capabilities that are essential for digital transformation are apprehended
• Business ecosystem role, the potential for BMI, and current BM are comprehended
• Capabilities are enhanced and cultivated for the AI execution
• Internal is developed and organizational acceptance is reached
• Tensor flow

Taking everything into account, the research scholars can grasp any innovative and latest PhD topics in computer science from our research experts. Consequently, we guide research scholars in all stages. In the same way, we make discussions with you at all stages of the research work. So, scholars can closely track the research work from everywhere in the world. Additionally, our well-experienced research professionals will provide significant assistance throughout your research process.

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How should someone choose a PhD topic so that they don't fail?

One of my close relatives went to Australia to get a Ph.D. in computer science. PhDs in Australia are 3 years long. She couldn't complete her Ph.D. in 3 years. Then she applied for an extension and got 1 year more. However, ultimately she failed. I asked her about the issue and she preferred to stay silent.

As far as I guess, she chose a topic that was destined for failure. I.e. her hypothesis was incorrect.

How should someone choose a Ph.D. topic so that they don't fail?

• computer-science

• 90 Failing the PhD because the topic was destined to failure is an advisor's failure. –  Massimo Ortolano Commented Oct 11, 2020 at 7:55
• 52 A failing hypothesis is not an ultimate reason for a Phd failure. Sure, papers on a hypothesis that proves to be false are harder to publish, but a Phds purpose is to certify that you can do sound scientific research. Proving that something plausible is not true is part of that. So while a failing hypothesis can be a hindrance it is not sufficient to cause failure in a Phd. –  Frank Hopkins Commented Oct 11, 2020 at 15:39
• 45 If you could predict "success" in "proving" an hypothesis three years in advance, you wouldn't be doing research. Research is exploring the unknown, not giving reasons for things known to be true. Alternatively, you would be doing some trivial exercise, going through motions to no useful end. –  Buffy Commented Oct 11, 2020 at 15:45
• 11 I'm not sure if any of my Aussie PhD friends finished within 3 years, and even taking longer than 4 years is very common. Did they actually fail, or have they just run out of funding? Do they need more funding? Is it now just time to finish writing up what they did? –  curiousdannii Commented Oct 12, 2020 at 4:52
• 37 This whole question seems very strange — the asker’s assumptions are wrong, and their motivation is unclear. It’s like asking “My friend got married, but then got divorced. I presume this is because her spouse was a bad cook. How do you make sure that your spouse is a good cook?” Sure, we can answer the specific question — “Have them cook for you earlier in the relationship.” — but is that really what you wanted to know, or was it “how can I make sure that my marriage will be good”, or “how can I tell my friend what she should have done differently”, or something else? –  PLL Commented Oct 12, 2020 at 12:00

9 Answers 9

Make sure your advisor has a good track record of graduating students in time.

Anyone just entering or outside the field won't be able to assess PhD topics with good judgement, so it's unfair when advisors fail their students by giving them bad projects. Your best bet at avoiding this is finding an advisor who is unlikely to fail students in this way.

If you find yourself in this position, a good bet is to reach out to other professors and tell them what's going on. It can feel shameful, but I've seen many success stories of people getting a new project and spinning it into enough for a PhD when things aren't working out with their initial advisor.

• 2 we do not know that the cases of the OP was that the project was bad. –  lalala Commented Oct 11, 2020 at 15:22
• 9 @lalala OP literally asked the question of how to avoid choosing a bad project. I never weighed in on the hypothesis about OPs relative, I just answered the question OP posed. –  Well... Commented Oct 12, 2020 at 5:59
• 4 This is it, choose your supervisor not your subject. A good research subject will be trashed by a poor advisor, and in any case your approach will be completely dominated by them. Look at what happened to their previous PhDs including their initial years post-doctorate, because they too are typically dominated by the PhD and supervisor. Younger supervisors are more cutting edge, driven and empathetic (by temporal proximity) to the PhD's situation. Older supervisors are more experienced and connected/senior in their field. Either kind may be abusive or destructive because academia allows though –  benxyzzy Commented Oct 13, 2020 at 6:30
• 1 Yes, the advisor's track record is the dominant feature. Sure, other things have some effect... but nothing else as much as this. –  paul garrett Commented Oct 13, 2020 at 17:42
• This is by far the best advise. That said, it's in practice sometimes hard to follow since the number of drop-outs is usually not easily accessible. I know people that graduate lots of students, some of the excellent, but I still would strongly advise against starting a PhD with them since they also have a really high dropout rate (i.e., the shotgun approach of research supervision). –  xLeitix Commented Aug 15, 2022 at 7:52

A PhD is awarded following submission of a thesis. It is extremely rare for a student who submits a thesis to fail. It is quite common for a student to never submit a thesis.

If the goal is simply to pass, then the key questions should be:

• What are the expectations for a thesis in my discipline? Expectations vary, but usually originality is expected.
• Will this thesis topic allow me to meet those expectations?

The one situation where a choice of topic would be likely to directly cause failure would be if the topic is blatantly not original. For example, it is found in well-known textbooks. It is much more common for a student to stop working on their thesis because they do not like the topic.

Financial and health factors are common causes of PhD non-completion.

• 3 usually originality is expected? There are exceptions to that? 8-( –  einpoklum Commented Oct 12, 2020 at 8:12
• 1 There are absolutely thesis topics with little failure potential. These are usually also recognizable from afar for anyone in the scientific community, so they are mostly useful if you need an academic title for political reasons but have no other academic interest. –  Simon Richter Commented Oct 12, 2020 at 17:00

Speaking as someone currently in the trenches, I’d advise the following general strategies for a doctoral student to maximize their chance for completion. At the very least, all these points should be considered. Also, as others have said, you won't fail a dissertation for having a hypothesis that yields a negative result – a dissertation is very much about the process not the scientific result per se.

• Develop your dissertation to play largely to your strengths, not address your weaknesses. For example, if you’re really strong at biological research but have only just learnt to code, it might not be a good idea to have a dissertation that is centered on building a software platform – even if it does target biological research as its domain.
• Choose a topic for which you’ll have expert guidance. That means your advisor and members of your committee can understand the concepts, methodology, and novelty of your work. Their advice will also be that much more helpful; they’ll be better equipped to help you navigate the roadblocks that’ll inevitably crop up.
• Do the background to make sure you’re addressing a real gap in the current literature. It pays to be a bit future thinking and aspirational; as a PhD student, one of the advantages you have is a multi-year timeframe where you can largely focus on one thing. Don't be afraid to think big and then narrow down your focus – doing so can help give you a larger sense of purpose; it can help you remember how the little thing you're working on in the moment factors into your larger vision.
• Discretize and make independent the goals of your project. This can be tough to do but is well worth it. Having each goal build on the other can amplify the risk that your entire project fails. For example, this could happen if you hit an insurmountable roadblock well past the timeframe where you can reasonably pivot your research direction. As a bonus, this strategy can also improve the odds that one of your research projects will have a meaningful impact.
• Be wary of situations and research designs that will precipitate bureaucratic delays. IRBs, data access committees, awaiting approval from distant stakeholders (timezone delays can add up!), and long duration data generation are examples of this. If at all possible, design your project to at least have a primary endpoint that won’t require more than one of these. Note that not all of these potential roadblocks are created equal. In my experience, the order of the above delays looks something like this: Long duration data generation > IRB > data access committees > distant stakeholders.
• Document communications and decisions with your committee and administration in writing. For example, when seeking input on a larger project decision from your committee members via email, be sure to state (in a friendly way) when you need a response by and the default action that will occur if no response is received by that date. Send a friendly reminder 48 hours before the date if you haven't received a response. For big decisions and reviews, allow your committee 2 weeks of lead time.
• Have an insurance policy. This is something I often setup before making a big career decision – ultimately, failure is always a possibility. What I mean by this is to have something to fallback on if your primary focus (i.e. your doctorate) ends in failure. As an example, I completed an MS prior to pursuing a doctorate and have a software side project and associated business plan that I believe are together legitimately valuable and actionable – at the very least, both would help me land a job that I would enjoy and keep me stable. Having 'insurance' can help give you peace of mind and sustained focus when pursuing something that might be inherently risky, and in some respects doctoral degrees are.

This isn't an exhaustive list; there are other considerations as discussed in other answers. That said, in my personal experience (and observing others at my institution) I’d recommend being mindful and discussing all of these aspects of your dissertation, possibly throughout your doctoral research, with your advisor and/or committee, though the latter may be best discussed with your peers.

• 6 "Go big" is a very dangerous suggestion -- it's very easy to overdo it and end up with something that is not realistically doable on a ph.d. timeframe. Again having the guidance of an advisor is of course crucial. –  Denis Nardin Commented Oct 11, 2020 at 17:43
• 4 "Scooped" isn't really relevant for a thesis. For a paper yes, but no committee would fail you because someone published something similar or the same. The point is you put in the work, got the result, and wrote it up in good faith. –  Azor Ahai -him- Commented Oct 11, 2020 at 23:07
• 1 @DenisNardin I agree – I misspoke, I've updated my answer to better communicate what I inteneded. –  Greenstick Commented Oct 12, 2020 at 0:06
• 3 True - just clarifying because the title question was about dissertations, specifically. –  Azor Ahai -him- Commented Oct 12, 2020 at 0:20
• 3 Many of these are great suggestions for research in general and could also benefit postdocs and early career professors. –  WaterMolecule Commented Oct 12, 2020 at 15:43

It is an empirical fact that the percentage of graduate students who fail to complete their PhDs is quite high.

It follows that there does not exist a simple algorithm for choosing your PhD topic that guarantees success - certainly not one that fits in the space of an academia.se answer. If it did exist, everyone would know it, and we wouldn’t see the numbers of people who start a PhD and don’t finish it that we do end up seeing.

Finishing a PhD is a matter of talent, a lot of hard work, and in some cases a bit of luck. It’s good to do some advance research on best practices for choosing an advisor and a topic, but no amount of preparation can save you the need to have some combination of those three things.

• 2 Do you have any statistics for the high rate of PhD dropout? In my experience, everyone I've known who started has completed, so that is quite surprising to me. But I do say this from the point of view of a student, rather than faculty. –  Bamboo Commented Oct 13, 2020 at 4:11
• 1 @Phill I don’t have statistics, sorry. –  Dan Romik Commented Oct 13, 2020 at 5:11
• 1 In all the grad math programs I've seen in the U.S., less than 10% of students do not complete their PhD, and most often they discover within a year or two of beginning that they don't want to do math... not that there's some obstacle otherwise. –  paul garrett Commented Oct 21, 2020 at 0:07

I want to reassure you that don't fail a PhD dissertation because your hypothesis was incorrect. If you are stressed about this on your own behalf- don't be . Your result is outside of your control.

To reassure you, null results are published all the time . For example, "The Ineffectiveness of using Generic Deep Learning approaches on Problems of Type XYZ" can be published in a great journal, so long as your readers still learn something important from your article. Here's what reviewers look for, in general:

• The methodology was sound.
• The argument that one could have expected your approach to work was clear and agreeable to your audience.
• The paper was well written and clearly outlays the conclusions and implications for the field that practitioners care about.

Having an interesting and successful thesis helps, no doubt, but it is not the sole issue here. The demands of 1,2,3 are very high.

That said, if your friend doesn't want to discuss why they did not complete their PhD, I would avoid poking at them. There are innumerable reasons why they might not have finished it, and it's best not to speculate . You may easily arrive at an incorrect conclusion. Heck, perhaps they dropped out because they got a great job offer as ABD.

As far as I guess, she chose a topic that was destined for failure. I.e. her hypothesis was incorrect. How should someone choose a Ph.D. topic so that she doesn't fail?

I think your hypothesis here might be incorrect. You could in theory write an entire PhD thesis based on an incorrect hypothesis. The entire point of the thesis would become disproving the hypothesis.

It's obviously not as satisfying as proving something is true, but it's valid science. If the original hypothesis was reasonably plausible, it means others won't have to repeat your mistakes.

And in any case, the point of a PhD is not so much to produce useful new science, as to produce a new scientist . I.e. someone who can demonstrate, through their thesis, that they understand the scientific process well enough to produce original results. It doesn't really matter, that most of the time, these original results are pretty useless! The originality is just a way to prove that the science came from them, and not someone else. It's only purpose is to demonstrate the following hypothesis: "Dr X is, indeed, a scientist"

Possible reasons for failure:

• lack of support from the advisor (a good advisor would advise how to turn that failing hypothesis into a successful thesis)
• mental breakdown of the student (it can be soul-crushing to spend so much time trying to get something to work, and failing)
• lack of time (if the people involved realise too late that "this isn't working", and lack the "narrative" skills to quickly turn that apparent failure into a success)

Note: people doing research in computer science can get a bit confused about what they are actually doing. Science is about asking questions, finding answers, and writing about them. So it can't really "not work". A negative result is still a result (unless you entire experimental set up got destroyed and your data corrupted, as long as you follow proper methods, you can't really fail) However, engineering would be about using science to produce a workable solution to a problem. Now this can very much fail. This is not what a PhD is about. But people can get misguided. Computer scientists ("I must write about computer science") who think that what they're doing is software engineering ("I must deliver working software"), are very much at risk of failing. And sometimes the way a PhD thesis is funded (e.g. industry grant) can fuel that misconception.

Note 2: re "originality", a very plausible cause of failure, is if you start your PhD on a valid original topic, but then someone else basically writes your thesis before you've finished it. This happens all the time... And is incredibly stressful/frustrating! Same problem with publishing papers. Some topics are popular, and great minds think alike... So it's really not that unusual for different people to be unknowingly working on the same hypothesis in parallel! And I honestly don't know what's the best way to avoid that situation, and to salvage your hard work, when someone else beats you to the finish line... (I guess try and publish anyway, even if originality takes a hit... E.g. introduce a small variation, etc. But all the extra testing and writing can really screw things up in term of timing, when grants are running out)

When one fails or is about to fail a Ph.D., it is worth understanding what requirements are not fulfilled. This may vary from a field to field but generally, there are four sets of requirements:

Formal criteria required by law. These are usually vague and the easiest to fulfill. They dictate the number of course points, seminars, and some generic requirements like "contribution to knowledge" etc. you have to fulfill to get a Ph.D.

Requirements by the university. These may specify the thesis format, specific courses to attend, teaching work, funding, etc.

Requirements by the community determine the level of quality that is considered good and worthy of publication by other researchers in the field.

Requirements by your supervisor. These are tricky because they are implicit. Inadvertently, you may get a very demanding or difficult supervisor, or, alternatively, you can have a very supportive one.

The exact thesis topic is largely irrelevant. As long as it broadly falls within CS (or any other study area) you are fine.

What matters is that a student knows the formal criteria. There should be quarterly/yearly evaluations and the supervisor/university should facilitate the student in attaining them.

Having publications of thesis work is a good sign that the work is of reasonable quality. Maintaining a good relationship with the supervisor help with understanding his/her expectations.

From my anecdotal and very limited experience, students fail PhDs for two reasons:

Difficult relationship with the supervisor due to misunderstood expectations, mismatch of personalities, inability to receive critical feedback, unwillingness to put in hard work, leading to..

Difficulties in publishing their results either due to preparing manuscripts taking forever or being repeatedly rejected from peer-reviewed venues. Lack of progress exacerbates #1

To conclude, the advice to anyone starting a PhD is to pick the supervisor carefully.

I am a professor, I have been on more than 20 doctoral committees. Most of the answers here are focused on, or call attention to picking a topic. IMHO - by itself this is not a good strategy.

In my experience, all dissertation decisions hang on one thing: the candidate's ability to understand how gatekeeping works. That is to say the classic error is the doc candidate who thinks they want their work to be great so they find the smartest people on campus to be on their committee. Translation: the four biggest egos in that discipline on campus are now on your committee. Good luck with that. Applying such a belief system (get the best and brightest) has the potential to inspire Intra-committee disagreements. That's risky. The worse-case output is the dissertation never gets done and it's not the candidate's fault.

IMHO if you want to create the most favorable conditions for graduating, research your potential committee chairs. 1) are they well-liked, respected? 2) research potential chair's doctoral committee history and records of how many successful/not successful dissertations 3) information interview your potential chair. 4) once chosen, ask your committee chair who should be on the committee.

The chair will likely recommend people who are agreeable with their ideas. Your committee meetings will be friendly. Don't get me wrong, you still have to find a good topic, be clever, and write well. A good advisor will steer you away from rough seas, heal weaknesses in your work, or advise strategies to keep your work relevant. IF you don't have that in your corner, you can still finish, it's just a lot more work to figure that stuff out on your own.

IMHO when it comes to topic and writing, buy or otherwise acquire a doctoral candidate or 'dissertation' handbook. Most universities have them in some form, usually found at the department level. Get one, read it, follow the guidelines laid out by your department -- and keep a journal of your committee meetings. Where you can, use the rules (and your notes) to your advantage.

The bottom line is that earning a phd requires you to pass through an institutionalized system. Such systems have rules and structures that can be learned and used to create pathways to success.

My experience on doctoral committees -- 20% of the dissertation ideas are not (and never will be) well conceived, 20% are exciting and interesting, The middle 60% are well-written -- or technically well-executed (and not so well-written), but otherwise good. Prolly 10% of candidates are rejected, and we always attempt to counsel our candidates to bail out early if we think they won't make it.

Good luck with your ambition. It's worth the effort. I was 20 years owner of a software company, now 20 years as professor.

My answer is basically for US as that's where I am from, and also where I got my Ph.D.

Advisor is key. Work with your advisor to pick an approved topic. The advisor will typically know what will work.

It is important that the PhD candidate's research have original research, but it also needs to be related to and compared to past research, so extending past research is important. For example, creating a new algorithm would obviously be original research. Finding statistical equations for existing algorithms would be extending past research.

It also helps to finish the work in a timely manner. Most PhD candidates have done enough reading, so it cannot be emphasized enough to write up the research. If it is possible to publish it or present it in a conference (these days, likely to be a virtual conference), this will also help.

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Career Guide: Electrical and Computer Engineer Jobs with a PhD

A PhD in Electrical and Computer Engineering is an elite credential.

It opens up a world of advanced technical roles at the forefront of innovation. Upon graduation, ECE doctorates can pursue fulfilling careers as research scientists, respected university professors, senior engineering experts, and executive-level technical leaders. 

With unparalleled knowledge and valuable research abilities, PhD graduates in electrical and computer engineering can elevate their earnings potential while preparing for coveted senior technical and academic positions.

If these opportunities interest you, it’s time to explore the top career paths and opportunities awaiting those with a PhD in electrical and computer engineering.

Overall ECE Salary Potential for PhD Graduates

According to Payscale , electrical and computer engineering PhDs have an average salary of $149,000 in 2021.

While the PhD journey requires substantial effort, this elite credential pays in the long run. ECE PhDs can expect premium salaries along with accelerated advancement into leadership roles thanks to their elevated expertise.

Electrical and Computer Engineering Jobs for PhD Grads

Research Scientist

As research scientists, ECE doctorates pearhead cutting-edge studies and development projects to solve complex technical challenges. Their primary responsibilities involve:

• Identifying problems or knowledge gaps
• Formulating hypotheses
• Designing and executing experiments
• Analyzing data
• Developing or enhancing technologies based on their findings.

Professionals with a PhD in electrical engineering earn an average salary of $137,525 per year as research scientists according to PayScale.  

Employers eagerly seek the extensive research training and specialized technical prowess that ECE PhDs gain through years of intensive study culminating in their doctoral dissertations. Their proven hands-on experience planning and executing original research from conception to publication gives them a distinct edge for research scientist roles.

Where you can work

• Major technology leaders like Microsoft, IBM, Intel, Apple, and Qualcomm
• Government agencies like NASA and the Department of Defense
• Top research universities and national laboratories
• Medical and biotech firms

Earning a PhD is a requirement for securing a tenure-track faculty position. As professors in university ECE departments, PhDs can:

• Teach courses
• Develop curricula
• Advise students
• Conduct research
• Publish scholarly papers
• Secure research grants
• Serve in administrative roles. 

Most especially, electrical and computer engineering professors impart advanced technical knowledge across areas like electronics, telecommunications, computer architecture, embedded systems, VLSI, photonics, and more — while earning an average of $144,115 per year, according to Salary.com .

Many top-ranked engineering programs actively recruit PhDs for ECE professor roles. Smaller colleges and universities also hire these doctorates to teach and conduct research.

Senior Hardware/Software Innovator

Senior hardware and software engineers are elite technical experts who lead complex, pioneering projects. With a PhD in ECE, engineers may head teams developing next-generation computer hardware including:

• Quantum hardware
• Biometric hardware
• Wearable ecosystems
• Sustainable technologies
• Embedded IoT devices
• Groundbreaking software for AI, machine learning, robotics, cybersecurity and more.

Salary.com data shows senior hardware engineers with PhDs earn $114,930 per year on average, while their senior software counterparts with doctorates average $120,906 to $126,967 annually .

The rigorous, hands-on doctoral research required provides hardware and software engineers with the specialized knowledge and advanced problem-solving abilities to succeed in senior technical roles. Their proven capacity to tackle complex challenges through novel research makes PhD holders very attractive hires.

Innovative technology leaders like Apple, Google, Nvidia, Qualcomm, AMD, and others aggressively recruit PhDs for elite senior engineering roles to accelerate the development of products and services.

Engineering Leader

At the director and VP levels, engineering leaders must have skills (and the credentials to prove it), so they can carry out their important work:

• Managing large technical teams
• Spearheading strategic initiatives. 
• Providing vital technical vision, guidance and mentorship in areas like computer architecture, hardware design, networking, embedded systems, and beyond.

According to Glassdoor, a Vice President of Electrical Engineering earns an average base salary of $140,000 to $241,000 per year.

Through expertise gained in their doctoral research, ECE PhDs position them well for executive engineering leadership at major corporations including:

• Lockheed Martin

Taking Advantage of Career Opportunities in ECE: Keys to Success

When launching their careers, ECE PhDs should look beyond the obvious company types and roles. Their rare combination of deep technical knowledge, research abilities, analytical skills, and problem-solving expertise has remarkable value across diverse industries.

• Networking: Building connections through your academic network can prove invaluable. Alumni associations, advisors, and professors can facilitate introductions to companies of interest
• Attending conferences: Events related to your specialization fosters great connections too.
• Nailing interviews:  Highlighting tangible achievements like published papers, patents, products, implemented solutions, and other concrete examples showcases your capabilities you bring employers.

A rewarding career is possible with the right education: SMU Moody's PhD in ECE program has you covered

Earning your PhD in computer engineering and electrical engineering is a “high-reward” degree. 

With the right information, a well-chosen program, and strong advocates by their side, ECE doctorates are perfectly positioned for professional success across academia, government, and virtually every industry in need of elite engineering expertise.

Looking for those things doesn’t have to be a hassle — download our resource, A Complete Guide to Earning Your Ph.D. in Electrical and Computer Engineering , and take the next step towards earning the degree that’s propelling society forward.

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