Computer Science PhD

Why choose this course

Our PhD research programme provides you with the opportunity to study a wide range of computer science topics - from technologies and systems for securing online businesses and our lives, to technologies inspired by nature for solving complicated real-world problems, and to real-world applications of advanced technologies such as machine learning and pattern recognition. Our expert supervisors, highly rated research activities and excellent facilities mean that by the time you complete your PhD, you will be a specialist in your field.

The Surrey Centre for Cyber Security (SCCS), hosted and managed by the Department of Computer Science, is one of only 14 UK Government recognised Academic Centres of Excellence in Cyber Security Research (ACEs-CSR), and is particularly strong in interdisciplinary research with other departments, with links with Electrical and Electronic Engineering, Psychology, Business, Sociology, Law, and Economics. We also have strong links with global research institutes such as the University of Oxford, Microsoft Research Cambridge and the Pirbright Institute for joint PhD projects.

What you will study

Our PhD in Computer Science will give you the knowledge, skills and abilities needed for a career in academia, research and development work in industry, and for a wide variety of professional opportunities.

It normally takes around three to four years to complete a full-time PhD. You’ll be assigned at least two supervisors (a principal and a secondary), who will guide you through your PhD. You’ll learn how to conduct literature reviews, how to develop your research ideas and verify them with experiments, and how to collaborate and perform interdisciplinary research.

Research themes
Secure systems

• Hardware security
• Trusted computing
• Privacy-preserving computing
• User authentication
• Secure communications
• Network security
• Multimedia security
• Digital forensics
• Human factors, etc.

Nature-inspired computing and engineering

• Computational intelligence
• Evolutionary optimisation
• Machine learning, including deep learning
• Pattern recognition
• Data mining
• Knowledge extraction, etc.