Lead supervisors for the Centre for Doctoral Training in Statistical Applied Mathematics
We are academic staff from the Department of Mathematical Sciences who may be available to supervise your PhD project as your lead supervisor.
Get in touch with us about PhD projects.
- Mathematical biology
- Infectious disease epidemiology and evolution
- Ecological modelling
- Statistical geometry
- Spatial analysis
- Survival analysis and statistical methods in epidemiology
- Mathematical Biology
- Ecology and evolution of infectious diseases
- Evolution of sex and mate choice
- Spatio-temporal modelling
- Functional data analysis for modelling high frequency time series
- Applications in epidemiology, ecology and environmental sciences
- Methods for handling missing data in statistical analyses
- Causal inference methods for randomised trials
- Biostatistical and epidemiological applications
- Industrial applied maths especially problems involving electricity, food or telecommunications.
- Numerical weather forecasting and data assimilation.
- Non smooth dynamical systems, friction, impact and chaos.
- Scale-interaction phenomena via asymptotic analysis of PDE
- Operator theory and functional models
- Applied calculus of variations
- Probability and applications in Mathematical Finance
- Stochastic optimal control
- Martingale optimal transport
- Dynamical systems (pattern formation, reaction-diffusion problems, bifurcation theory)
- Networks and dynamics
- Fluid mechanics (nonlinear phenomena, asymptotic methods)
- Analysis of nonlinear partial differential equations
- Blow-up patterns in nonlinear evolution problems
- Singular limits in variational problems with loss of compactness
- Generalised Linear Models: Modelling, Approximate Methods, Value of Information
- Spatial and Spatial-Temporal Geostatistics: Modelling, Sampling Design
- Time Series: Modelling, Sequential Analysis
- Numerical linear algebra and scientific computing
- Approximation and reduction of multivariate functions and tensors
- Probabilistic and quantum modelling
- Inverse problems (e.g. models, algorithms)
- Large-scale, randomized optimization (e.g. convergence guarantees, rates)
- Applications (e.g. imaging, machine learning, deep learning
- Asymptotic analysis and perturbation methods
- Industrial and applied mathematical modelling
- Complex fluids with memory, high order nonlinear evolutionary PDEs and free boundary problems
- Functional data analysis
- Shape Statistics
- Applications of Statistics
- Harmonic Analysis (commutative and non-commutative)
- Lie groups, homogeneous domains, representation theory
- Pseudo-differential operators and Partial Differential Equations
- Inverse problems and regularization
- Image restoration and reconstruction
- Numerical linear algebra, Krylov subspace methods
- Analysis and solvers for high frequency wave problems
- PDEs with random input data and UQ
- PDE eigenvalue problems and reactor stability
- Mathematical control theory
- Applications in mathematical biology
- Positive systems
- Sequential Monte Carlo and Markov Chain Monte Carlo methods
- Martingales and Markov processes
- Computational methods in population genetics
- Tropical mathematics
- Numerical linear algebra (applications of tropical mathematics, algorithms which exploit randomization)
- Data science (applications of tropical mathematics, machine learning)
- Models arising from statistical physics, with an emphasis on understanding critical phenomena
- Abelian sandpile model of self-organised criticality
- Behaviour of random walks on fractal graphs
- Complex stochastic models
- Markov Chain Monte Carlo samplers
- Adaptive and group sequential clinical trials
- Reinforced random walks, self-interacting processes
- Random walks in random environment, or in dynamical environment
- Reinforcement learning
- Self-similar processes, Lévy processes and their applications
- Spatial branching, fragmentation and coalescing processes
- Stochastic (numerical) modelling
- Mathematical control theory
- Differential equations
- Stability and stabilization
- Branching processes
- Pólya's urns and stochastic approximation
- Random networks
- Averaging and Homogenisation for PDEs
- Infinite-dimensional dynamics: PDEs and lattice ODEs
- Many particle dynamics
- Geophysical fluid mechanics and conservation laws
- Nonlinear waves and free-surface problems
- Mathematical biology
- Scientific computing, HPC and novel architectures
- Fast solvers for partial differential equations in atmospheric fluid dynamics
- Algorithms and software for stochastic differential equations and molecular dynamics
- Partial differential equations and nonlinear analysis
- Concentration phenomena in nonlinear elliptic equations
- Blow-up in nonlinear parabolic equations
- Wavelets and lifting schemes
- Time series, image and network analysis
- Bayesian Computation
- Model reduction
- Control theory
- Analysis
- Stochastic analysis with applications in biology
- Random networks
- Stochastic processes in random environment
- Probabilistic models motivated by population genetics
- Spatial branching processes with interactions
- Applications of probability theory to partial differential equations
- Pure and applied probability
- Stochastic Geometry
- Random graphs, percolation and interacting particle systems
- Inverse problems and compressed sensing
- Machine learning and optimisation
- Infinite dimensional regularisation
- Numerical methods for geophysical fluid problems
- Natural Disasters
- Automated computational adaptive algorithms
- Probability
- Branching processes: branching Brownian motion, branching random walks
- Random graphs, random environments
- Graphs and networks
- Applied stochastic processes
- Emergent phenomena
- Statistical analysis of networks and graphical models
- High dimensional inference
- Optimization Methods
- Analysis, Partial differential equations, and Applied mathematics
- Modelling of biological systems and Numerics
- Geometric analysis
- Stochastic PDEs and their applications
- Langevin equations
- Numerical methods for strong and weak approximation
- Bayesian networks and uses of conditional independence
- Bayes linear methods
- Analysis of collections of (second-order) exchangeable sequences
- Variational problems
- Applied analysis, Partial differential equations
- Nonlinear elasticity, fluid mechanics
- Methods for spatial and spatio-temporal data
- Computation for Bayesian methods
- Applications in the public health and the social sciences
- Large Sparse Matrix Computations and Eigenvalue Problems
- Hopf Bifurcations in Mixed FEM Methods for N-S problems
- Network simulations in Bioinformatics
- Propagation of acoustic and electromagnetic waves
- Transform methods for linear and nonlinear integrable PDEs
- Problems at the interface between analysis and numerical analysis of PDEs
- Asymptotic analysis and perturbation theory
- Industrial and applied mathematical modelling
- Fluid dynamics and free-surface flows
- Stochastic partial differential equations
- Rough path theory and regularity structures
- Statistical mechanics
- Using mathematical models to explore problems in healthcare
- Non-invasive drug monitoring and infectious disease control
- Behaviours of network systems
- Mathematical modelling of biological systems in which stochasticity plays an important role.
- Efficient stochastic modelling and simulation methodologies.
- A range of biological application areas: (e.g. cell migration, embryogenesis, Collective animal behaviour, parasite dynamics, pattern formation).
- Multiscale analysis
- Dynamical systems and differential equations
- Scale-bridging