Durham’s Department of Mathematical Sciences was ranked joint 1st in the UK for internationally excellent and world-leading research impact in the last REF report (2014), and we expect to remain at or near that point when the 2021 report is published.
The Department’s active growth in recent years has embraced both staff and students, steadily increasing our ability to lead the way in research across the mathematical sciences. This in turn supports our research-led teaching, enabling us to continue to provide a top quality student learning experience which stays at the forefront of progress.
The Department’s 73 permanent researchers are organised into groups interacting with each other and other departments. We also host numerous postdoctoral and teaching fellows, including 3 prestigious Willmore Fellows. We aim to allocate a graduate student to our newest appointments and are constantly seeking funding to reach a target of two PhD students per member of staff.
The Applied and Computational Mathematics Group has a wide range of interests, primarily in the mathematical analysis of partial differential equations and in magnetohydrodynamics.
The Mathematical and Theoretical Particle Physics Group’s research activities fall into the broad categories of quantum field theory, string theory and gravity, cosmology and solitons in field theory. The group's interests complement those of particle physicists belonging to the Institute for Particle Physics Phenomenology (IPPP), and together we form the Centre for Particle Theory (CPT).
Research interests in the Probability Group include complex stochastic systems and phase transitions, stochastic processes and their applications, and random geometric, algebraic and combinatorial structures.
The Pure Mathematics Group’s areas of research include analysis, differential and hyperbolic geometry, dynamical systems, number theory, representation theory, topology and many interactions of these areas both within and outside of mathematics.
In the Statistics Group, research interests include: Bayesian and Bayes linear methodology, foundations of statistics and decision theory including imprecise probability, non-parametrics, smoothing and predictive inference, statistical modelling of structure, geometry and shape, uncertainty quantification for computer models and a wide range of applications, many large-scale.
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