Computing PhD

• benefit from a supervisory team comprising two or sometimes three members of academic staff. Depending on your research specialism you may also have an additional supervisor from another school, another research institution, or an external partner from government or industry.
• be provided with desk space and access to a desktop PC, either in one of the postgraduate offices on the 6^th floor of the award-winning Cockcroft Building, or within the adjacent Huxley Building.
• benefit from access to a range of electronic resources via the University’s Online Library, as well as to the physical book and journal collections housed within the Aldrich Library and other campus libraries.
• be able to use various state-of-the-art research facilities on the Moulsecoomb site.

Our research environment boasts the Centre for Secure, Intelligent and Usable Systems and the Applied Data Analytics Research and Enterprise Group which offer world-leading expertise in these areas. PhD students are active full members in these two research groupings, contributing to events and activities.

Moreover, the Division offers a range of subject-specific training opportunities (currently undergoing review and development) to complement the University’s more general research skills training programme for doctoral students.

The School of Architecture, Technology and Engineering hosts weekly Computing Research Seminars with a line-up of external and internal speakers, in which PhD students are encouraged to participate, both by attending and presenting seminars. Coming together for these seminars ensures broad knowledge of state-of-the-art research on a variety of topics, and enables staff and students to network and engage with other researchers in a professional context.

We actively encourage students to contribute to our strong and vibrant research community by publishing their research and attending external conferences around the world, for which funding is available. Our research themes include:

• 3D and 2D visualisation and digitisation: digital fabrication, virtual and immersive environments, visual analytics
• Applied data analytics methods for image, text and structured numerical data, in particular for health, business intelligence and remanufacturing applications
• Diagrammatic visualisation, logics and reasoning: visual ontology engineering, software and system modelling, usability of diagrams
• Knowledge engineering: knowledge-based AI methods, ontology engineering, repository architectures and semantic metadata
• Natural language generation: neural-network methods for text generation, data-to-text, automatic image description, evaluation
• Security, privacy, and trust engineering: computer security, intrusion detection, cyber situational awareness, blockchain security and privacy
• Text analytics: relation extraction, document classification, sentiment analysis, methods for messy user-generated text
• User experience: user modelling, user profiling and recommender systems

Dr Alexey Chernov

I am interested in supervising postgraduate research projects related to mathematically intensive methods of machine learning. My main area is sequential online forecasting, particularly prediction with expert advice. I am also interested in reinforcement learning (especially as a generalisation of online forecasting), kernel methods and topological data analysis.

Dr Andrew Fish

I am interested in supervising across a wide range of areas in Mathematics and Computing, on topics with a visual aspect. For example, areas could be in knot theory (Mathematics), or visual representations, languages, models and interfaces, with application domains such as security and privacy (Computing). I have experience of successful co-supervision across a wide range of areas/disciplines and am a UKCGE Recognised Research Supervisor.

Dr Michalis Pavlidis

If you are interested in a PhD under my supervision and you are willing to work hard with me in my research area (cyber security and privacy), please feel free to contact me. Several thesis topics, as well as MSc and undergraduate projects, are available.

Dr Karina Rodriguez Echavarria

My research interest includes the documentation and visualisation of heritage collections, embedding semantics and knowledge to heritage 3D digital assets as well as the use of 3D documentation for producing 3D printable products to support the exhibition and conservation of heritage artefacts. Research topics for supervising includes:

i) 3D digitisation/imaging and 3D digital collections

ii) generating complex, diverse and linked data resources,

iii) semantic technologies, including shape analysis, shape recognition, search and browse, 

iv) community involvement with heritage by the use of mobile technologies and social media,

v) design and engineering of objects by digital fabrication technologies.

vi) Sustainability and business aspects for the digitisation of heritage assets, including blockchain technologies and NFT

Dr Anestis Touloumis

I am interested in supervising postgraduate research students in categorical data analysis, multivariate analysis and high-dimensional statistics. 

Dr Simon Walters

I’m interested in research supervision in: electrical, electronic, automotive, mechanical and aeronautical engineering, including computer science - intelligent systems applications. I have particular interest in: power and high voltage systems, power electronics, intelligent systems - neural networks and fuzzy logic.

PhD Students:

 Name

 Thesis

Rama Rahimi (2022-present)

TBA

Bartosz Płoszaj (2020-present)

TBA

Alan Thomas (2014–present)

Intelligent optimisation techniques for minimising transient vehicle emissions

Shaun H Lee (2011) 

Intelligent techniques for improved engine fuel economy

Merrenna Manula De Zoysa (2003) 

Neural network estimation of air-fuel ratio in internal combustion engines 

Nicolas Miché (2002)

Gas Flow Analysis of a Uniflow Diesel Aero-engine

Dr Marcus Winter

I am interested in supervising postgraduate research students in ubiquitous computing, human-computer interaction and applied machine learning in the contexts of education, cultural heritage and public engagement.

For further supervisory staff including cross-disciplinary options, please visit research staff on our research website.