Mathematics and statistics PhD

As a mathematics and statistics PhD student at Brighton, you will 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. 
• desk space and access to a desktop PC, either in one of the postgraduate offices on the 6th floor of the award-winning Cockcroft Building, or within the adjacent Huxley Building.  
• 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. 
• use  of various state-of-the-art research facilities on the Moulsecoomb site.

You will be part of a small group of mathematics and statistics PhD students, thereby having excellent access to supervisory teams, whilst being situated amongst a larger cohort of PhD students within the university, with school-based students across computing and engineering facilitating further potential for interdisciplinary interactions.

The Brighton Doctoral College offer a training programme for postgraduate researchers, covering research methods and transferable (including employability) skills. Attendance at appropriate modules within this programme is encouraged. Participation in ongoing subject-specific seminar programmes at the University of Brighton is also highly recommended.

We are particularly interested in projects that align to one of our specialist themes:

• The Computational methods theme focuses on the development of numerical methods for solving problems in applied mathematics and engineering. Avenues of interest include finite element methods, boundary element methods and the machinery which underpins these, such as numerical methods of linear algebra. These methods have been applied to problems as diverse as modelling the sound field radiated from a loudspeaker to modelling the formation and growth of cavities in the spinal cord.
• The Discrete Mathematics theme is interested in the development and application of discrete mathematical techniques, particularly as applied within computer science. Areas of interest include logic, formal languages, algebraic structures, algorithms, graph theory, combinatorial and computational geometry and topology. These methods have been applied to the development of visual logics to assist human reasoning, the development of information visualisation algorithms which automatically generate diagrams, and efficient knot detection with biological applications.
• The statistics and operational research theme has a wide range of research interests from theoretical probability to applied statistics and rigorous machine learning. Examples of current work are the application of financial and operational research methods for pricing financial and energy instruments and analysing investment incentives, the development of statistical methodologies for analysing big data studies, the development of marginal regression models with correlated discrete response variables, the theoretical analysis of online prediction models and the application of topological data analysis methods.

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 Paul Harris

I supervise PhD students who use numerical methods (such the boundary element method and finite element method) to solve problems in applied mathematics.

PhD projects that I am interested in supervising include using combined boundary element and finite element methods for modelling the motion of biological cells due to chemotaxis, the use of the finite element method to model deformations of the spinal cord (in relation to the medical condition syringomyelia) and the use of the finite element method in image analysis (such denoising images and segmenting images).

Dr Anestis Touloumis

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

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