Mathematics and statistics PhD

In taking your PhD in mathematics with the University of Brighton, you are joining a research culture that focuses on developing and applying theory and methods to help solve real world problems.

This drives and informs the research through three themes:

Computational methods
Discrete mathematics
Statistics and operational mathematics.

We also welcome mathematics and statistics PhD students with interdisciplinary projects, offering a wealth of close interaction with the university's science departments. We have had many successful PhDs that connect with areas such as biology (e.g. big data analysis) and computing (e.g. visualisation).

We have a strong reputation in these areas, which is strengthened by collaborations with many UK and international academic institutes (e.g. University of Liverpool; Durham University; Royal Holloway, UK; University of Rome, Italy; University of Montepellier, France) and industrial partners (e.g Hurstwood Park Neuroscience Centre, Haywards Heath, UK; Southern Water, UK; and Poste Italiane, Napoli, Italy).

Many of our graduates with a PhD in Mathematics, Statistics or Operational Research from the University of Brighton stay within a research and educational setting, predominantly by securing post-doctoral research positions or lecturing roles within various UK institutes, but also by transitioning into mathematics teaching in schools.

Apply to computing, engineering and maths in the portal

Key information

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.

Academic environment

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. You will be aligned with the Computing and Mathematical Science Research Excellence Group.

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.

Some of our supervisors

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 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 Barbara S. Lancho Barrantes

My area of expertise is Bibliometrics and Scientometrics. I would be interested in supervising PhD/Master/Undergraduate students at any angle of Bibliometrics and Scientometrics and linking them with data analytics and data science. Some examples of specific potential topics (but are not limited to):

Analyse and examine the relation between economic factors and the amount of scientific productivity within countries, academic institutions, geographic regions and subject areas.
Examine the citation flows between countries or organisations that collaborate.
Analysis of Scientific Research Collaborations (institutional, national, international)
Research on Sustainable Development Goals (SDGs)
Cyber Security, theoretical and practical aspects
Bibliometric horizon scanning methodology for identifying emerging topics.
Analyse the relationship between international collaborations and the mobility of researchers.
Conduct gender analyses based on publication data, especially in the context of the SDGs.
Creation and implementation of new scientometric indicators in open research infrastructures.
Examine the open-access citation advantage.
Analysis of predatory publishing as a threat to research integrity.
Understand the research profiles of Higher Education Institutions and their connection with scientific productivity.
Bibliometric mappings of countries, institutions, disciplines, etc.

These topics are general and can be more detailed once the students show an interest in any of them. I will consider any new proposals and discuss any ideas that come up.

In my papers, I analyse the citation increment among collaborating countries. I have also examined how citation patterns of disciplines and bibliographic database coverage may influence the citation flows between subject areas.

The impact of economic factors on countries' research productivity has been the subject of my recent publications. I used data from citation databases, UNESCO, and the OECD for this purpose. I confirmed the relationship between investment in research and scientific results using multiple linear regression.

As well as understanding the scientific contribution to Sustainable Development Goals (SDGs), I focus my research on the mechanisms to interpret this data, since different databases can produce different results.

Dr Anestis Touloumis

I am interested in supervising postgraduate research students in machine learning methods, categorical data analysis, multivariate analysis and high-dimensional statistics. Projects are likely to involve development of open-source software to support and disseminate the findings of the research.

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

Making an application

Once you have prepared a first-rate application you can apply to the University of Brighton through our online application portal. When you do, you will require a research proposal, references, a personal statement and a record of your education.

You will be asked whether you have discussed your research proposal and your suitability for doctoral study with a member of the University of Brighton staff. We strongly recommend that all applications are made with the collaboration of at least one potential supervisor. Approaches to potential supervisors can be made directly through the details available online. If you are unsure, please do contact the Doctoral College for advice.

Please visit our How to apply for a PhD page for detailed information.

Fees and funding

Funding

Undertaking research study will require university fees as well as support for your research activities and plans for subsistence during full or part-time study.

Funding sources include self-funding, funding by an employer or industrial partners; there are competitive funding opportunities available in most disciplines through, for example, our own university studentships or national (UK) research councils. International students may have options from either their home-based research funding organisations or may be eligible for some UK funds.

Learn more about the funding opportunities available to you.

Tuition fees academic year 2024–25

Standard fees are listed below, but may vary depending on subject area. Some subject areas may charge bench fees/consumables; this will be decided as part of any offer made. Fees for UK and international/EU students on full-time and part-time courses are likely to incur a small inflation rise each year of a research programme.

MPhil/PhD
	Full-time	Part-time
UK	£4,786	£2,393
International (including EU)	£15,900	N/A
International students registered in the School of Humanities and Social Science or in the School of Business and Law	£14,500	N/A

PhD by Publication
Full-time	Part-time
N/A	£2,393

Contact Brighton Doctoral College

To contact the Doctoral College at the University of Brighton we request an email in the first instance. Please visit our contact the Brighton Doctoral College page.

For supervisory contact, please see individual profile pages.