Before applying to the DAMTP PhD you may wish to make informal contact with potential supervisors but that is not compulsory. It will help our consideration of your application to know with whom you are interested in working and in what fields. This does not necessarily have to be narrowed down to a single supervisor or research area.
Contact details may be found on each supervisor's webpage. You are encouraged to make initial contact by email, and to provide a an up-to-date CV and brief explanation of your areas of interest.
Applied and Computational Analysis
Astrophysics
Fluid Mechanics, Geophysics, Biophysics and Soft Matter
Machine Learning (fundamentals and applications to healthcare)
Quantum Information
Mathematical Biology
High Energy Physics
General Relativity and Cosmology
Applied and Computational Analysis
| Supervisor | Interests | Taking students for 2027 |
|---|---|---|
| Natalia Berloff | Physics of information, physical neural networks, coherent quantum systems and physics-inspired, physics-based and quantum-enhanced computing. | Yes |
| Colm-cille Caulfield | Generalized stability theory for shear flows, stratified turbulence and mixing. Data-driven | Yes |
| Matthew Colbrook | Foundations of AI, data-driven dynamical systems, spectral problems, PDEs, optimisation | Yes |
| Nilanjana Datta | Quantum information theory and some aspects of mathematical physics. | No |
| Nicola De Maio |
Models and algorithms for large-scale inference of genome evolution and pathogen transmission |
Yes |
| Hamza Fawzi | Convex optimization and applications to quantum information theory, quantum many-body theory; Semidefinite programming relaxations and sums of squares | Possibly |
| Mark Girolami | Computational Statistics, Uncertainty Quantification, Machine Learning | Yes |
| Gamze Gursoy | Privacy-preserving computation and secure analysis of genomic data. Machine learning and algorithmic methods for genomics and multi-omics, and integration of genomic and clinical data for patient phenotyping | Yes |
| Anders Hansen | Functional Analysis, AI, Foundations of Computational Mathematics, Solvability Complexity Index hierarchy and computer assisted proofs, Hardness of Approximation in PDEs, Optimisation and Inverse Problems | Yes |
| Duncan Hewitt | Modelling of granular and other non-Newtonian flow problems | Yes |
| Adrian Kent | Quantum information theory and applications, quantum foundations, quantum theory and gravity, experimental tests | Possibly |
| Rich Kerswell | General stability theory, transition and turbulence in geophysical (e.g. librating flows) and astrophysical contexts; Non-Newtonian particularly polymer flows (drag reduction, elasto-inertial and elastic turbulence); noisy systems with non-normal properties. | Yes (PhD only) |
| Nigel Peake | Acoustics, fluid-structure interaction, stability of high-speed flows | |
| Michael Roberts | Imaging, clinical applications, machine learning | Unlikely |
| Carola Schonlieb | Mathematical imaging, inverse problems, applied PDEs, machine learning, optimisation | Yes |
| George Stepaniants |
Scientific machine learning, AI for scientific discovery, data-driven dynamical systems, inverse problems, statistical learning, and optimal transport |
Yes |
| Edriss Titi |
Rigorous Applied Analysis of Nonlinear Partial Differential Equations, including the Navier-Stokes and Euler equations, Atmospheric and Oceanic Dynamics Models, Data Assimilation, and Infinite Dimensional Dissipative Dynamical Systems. |
Yes |
| Mihaela van der Schaar |
Machine Learning and Artificial Intelligence |
Yes |
| Nicole Shibley | Physics of climate/environmental/planetary processes: ocean physics and mixing, particularly in polar regions; ice processes; ice-ocean interactions in Earth and planetary environments; building ventilation. Observational analyses using canonical and AI approaches, analytical/numerical theory, laboratory experiments | Yes |
Astrophysics
Information for PhD applicants intending to apply to this area is available through this link.
| Supervisor | Interests | Taking students for 2027 |
|---|---|---|
| Miles Cranmer | Machine learning for astrophysics, turbulence, planet formation, galactic dynamics, galaxy formation, astrostatistics, cosmology | Yes |
| Giulio Del Zanna | Atomic physics calculations and modelling applied to astrophysical plasma, spectral diagnostic techniques using emission lines to measure the plasma state, and analysis of data of the solar atmosphere from space-based and ground-based missions | Possibly |
| Henrik Latter | Instabilities, waves, and turbulence in astrophysical settings; Protoplanetary disks and planet formation; Magnetic fields and dynamos | Yes |
| Gordon Ogilvie | Astrophysical fluid dynamics of discs, planets and stars; waves, instabilities and magnetic fields in rotating fluids; nonlinear dynamics and asymptotic methods | Yes |
| Roman Rafikov | Astrophysical fluid dynamics, Accretion discs, N-body dynamics, Planet formation, Planetary dynamics, High Energy Astrophysics | Yes |
| Nicole Shibley | Physics of climate/environmental/planetary processes: ocean physics and mixing, particularly in polar regions; ice processes; ice-ocean interactions in Earth and planetary environments; building ventilation. Observational analyses using canonical and AI approaches, analytical/numerical theory, laboratory experiments | Yes |
Fluid Mechanics, Geophysics, Biophysics and Soft Matter
Information for PhD applicants interested in Soft Matter is available through this link.
Information for PhD applicants interested in Solid Mechanics is available through this link.
Information for PhD applicants interested in Waves is available through this link.
| Supervisor | Interests | Taking students for 2027 |
|---|---|---|
| Ronojoy Adhikari | Statistical physics, soft matter physics, continuum mechanics, stochastic processes, Bayesian inference and probabilistic machine learning, numerical solutions of partial differential equations, numerical functional minimisation, epidemiological modelling, applications of machine learning in the digital humanities | Yes |
| Natalia Berloff | Physics of information, physical neural networks, coherent quantum systems and physics-inspired, physics-based and quantum-enhanced computing. | Yes |
| Rajesh Bhagat | Interfacial flows, building ventilation flow, airborne disease transmission | Yes |
| Michael Cates | Statistical physics of soft and active matter | No |
| Colm-cille Caulfield | Generalized stability theory for shear flows, stratified turbulence and mixing. Data-driven |
Yes |
| Miles Cranmer | Machine learning for fluid dynamics, simulation, surrogate modelling, multiscale physics, astrophysical fluids, planet formation |
Yes |
| Stuart Dalziel | Fluid mechanics, frequently with a component of laboratory experiments as well as theory/numerics, with motivation ranging from geophysical and environmental flows to industrial flows and granular materials | Yes |
| Stephen Eglen | Computational neuroscience, neuroinformatics | Yes |
| Julia Gog | The mathematics of infectious disease | Yes |
| Ray Goldstein | Biological physics and fluid dynamics, theory and experiment | No |
| Peter Haynes | ||
| Duncan Hewitt | Modelling of granular and other non-Newtonian flow problems | Yes |
| Robert Jack | Statistical physics, rare events, soft matter, glassy dynamics and metastability | Yes |
| Maziyar Jalaal |
Biological Physics of Cells, non-Newtonian Fluids Mechanics, Environmental Fluid Mechanics, Rheology |
Yes (including MPhil by Thesis Students) |
| Rich Kerswell | General stability theory, transition and turbulence in geophysical (e.g. librating flows) and astrophysical contexts; Non-Newtonian particularly polymer flows (drag reduction, elasto-inertial and elastic turbulence); noisy systems with non-normal properties. | Yes (PhD only) |
| Quentin Kriaa | Environmental and geophysical fluid mechanics, often designing original experiments as well as analytical or numerical idealized models, to analyse multiphase flows - deep convective clouds; aerosol deposition and transport; extreme precipitations; avalanches; supercooled rivers; crystallizing magma; solidifying planetary cores. | Yes |
| Henrik Latter | Instabilities, waves, and turbulence in astrophysical settings; Protoplanetary disks and planet formation; Magnetic fields and dynamos | Yes |
| Eric Lauga | Fluid dynamics of biological and living systems; Biological physics and complex flows; Viscous and non-Newtonian fluid mechanics; Microfluidics; Active matter; Physics and mathematics of sports | Yes |
| Adrien Lefauve | Turbulence, stratified flows, laboratory experiments and modelling, coastal oceanography in particular in the context of climate change | Yes |
| John Lister | Stokes flows and lubrication theory, particularly driven by surface tension or with elastic deformation | Possibly |
| Gos Micklem | Computational biology | Possibly |
| Alison Ming | Atmospheric dynamics, coupling to radiation and chemistry, stratospheric processes and coupling to troposphere and surface, mechanistic frameworks. | Yes |
| Jerome Neufeld | River seepage erosion (theory and experiments), subglacial hydrology and the grounding zone, granular erosion, cryosphere repair | Yes |
| Gordon Ogilvie | Astrophysical fluid dynamics of discs, planets and stars; waves, instabilities and magnetic fields in rotating fluids; nonlinear dynamics and asymptotic methods | Yes |
| Nigel Peake | Acoustics, fluid-structure interaction, stability of high-speed flows | |
| Roman Rafikov | Astrophysical fluid dynamics, Accretion discs, N-body dynamics, Planet formation, Planetary dynamics, High Energy Astrophysics | Yes |
| Sebastian Schemm | Atmospheric fluid dynamics, large-scale circulation, jet stream and storm tracks, Rossby and gravity waves, extreme weather, Lagrangian methods, modelling of weather and climate, hybrid AI-physics models, machine learning parameterisations, parameter estimation with inverse methods, very high-resolution modelling | Yes |
| Nicole Shibley | Physics of climate/environmental/planetary processes: ocean physics, particularly in polar regions; ice processes; ice-ocean interactions in Earth and planetary environments; building ventilation. Observational analyses, analytical/numerical approaches, laboratory experiments | Yes |
| John Taylor | Fluid dynamics of the ocean, using numerical simultions, data analysis, analytical techniques | Yes |
| Edriss Titi |
Rigorous Applied Analysis of Nonlinear Partial Differential Equations, including the Navier-Stokes and Euler equations, Atmospheric and Oceanic Dynamics Models, Data Assimilation, and Infinite Dimensional Dissipative Dynamical Systems. |
Yes |
Machine Learning (fundamentals and applications to healthcare)
| Supervisor | Interests | Taking students for 2027 |
|---|---|---|
| Natalia Berloff | Physics of information, physical neural networks, coherent quantum systems and physics-inspired, physics-based and quantum-enhanced computing. | Yes |
| Miles Cranmer | Machine learning for physical sciences, ML-accelerated simulation, foundation models for science (polymathic-ai.org/), AI for scientific discovery | Yes |
| Carola Schonlieb | Mathematical imaging, inverse problems, applied PDEs, machine learning, optimisation | Yes |
| George Stepaniants | Statistical learning, optimal transport, AI for scientific discovery, scientific machine learning, inverse problems, and data-driven dynamical systems | Yes |
| Mihaela van der Schaar |
Machine Learning and Artificial Intelligence (formal applications through the University's Applicant Portal should only be made to work with her once a place has been confirmed), Machine Learning for Healthcare, Machine Learning for Education, Reality-Centric AI |
Yes |
Quantum Information
| Supervisor | Interests | Taking students for 2027 |
|---|---|---|
| Benjamin Beri | Condensed matter theory, topological order, quantum dynamics, quantum computing | Possibly |
| Natalia Berloff | Physics of information, physical neural networks, coherent quantum systems and physics-inspired, physics-based and quantum-enhanced computing. | Yes |
| Angela Capel Cuevas | Quantum information theory and quantum many-body systems. In particular, quantum Markov semigroups, functional inequalities, correlations on Gibbs states, local Hamiltonians, entropies and entropic inequalities, and applications to quantum algorithms and phases of matter. | Yes |
| Nilanjana Datta | Quantum information theory and some aspects of mathematical physics. | No |
| Hamza Fawzi | Convex optimization and applications to quantum information theory, quantum many-body theory; Semidefinite programming relaxations and sums of squares | Possibly |
| Boris Groisman | Foundations of Quantum Mechanics and Quantum Information Theory | No |
| Adrian Kent | Quantum information theory and applications, quantum foundations, quantum theory and gravity, experimental tests | Possibly |
| Frank Verstraete | Quantum entanglement and its role in many-body physics | Yes |
Mathematical Biology
| Supervisor | Interests | Taking students for 2027 |
|---|---|---|
| Nicola De Maio | Models and algorithms for large-scale inference of genome evolution and pathogen transmission | Yes |
| Stephen Eglen | Computational neuroscience, neuroinformatics | Yes |
| Julia Gog | The dynamics of infectious disease | Yes |
| Ray Goldstein | Biological physics and fluid dynamics, theory and experiment | No |
| Gamze Gursoy | Privacy-preserving computation and secure analysis of genomic data, machine learning and algorithmic methods for genomics and multi-omics, and integration of genomic and clinical data for patient phenotyping | Yes |
| Anders Hansen | Functional Analysis, AI, Foundations of Computational Mathematics, Solvability Complexity Index hierarchy and computer assisted proofs, Hardness of Approximation in PDEs, Optimisation and Inverse Problems | Yes |
| Eric Lauga | Fluid dynamics of biological and living systems; Biological physics and complex flows; Viscous and non-Newtonian fluid mechanics; Microfluidics; Active matter; Physics and mathematics of sports | Yes |
| Gos Micklem | Computational biology | Possibly |
| Carola Schonlieb | Mathematical imaging, inverse problems, applied PDEs, machine learning, optimisation | Yes |
| Ben Simons | ||
| Mihaela van der Schaar |
Machine Learning and Artificial Intelligence |
Yes |
High Energy Physics
| Supervisor | Interests | Taking students for 2027 |
|---|---|---|
| Ben Allanach | HEP phenomenology | No |
| Alejandra Castro | String Theory, AdS/CFT, and Quantum Gravity | No |
| Nick Dorey | Quantum Field Theory, String Theory and M-Theory | No |
| Maciej Dunajski | Mathematical Physics, Twistor Theory, General Relativity, Solitons | Yes |
| Ron Reid-Edwards | Quantum Gravity, String Theory and Quantum Field Theory. | Yes |
| Ben Gripaios | Mathematical approaches to Quantum Field Theory; Theory beyond the Standard Model | Yes |
| Sean Hartnoll | Holographic emergence of space and time in quantum gravity | Maybe |
| Sven-Ludwig Krippendorf | Machine Learning for Theoretical Particle Physics and Cosmology, Physics of learning | Yes (PhD only) |
| Enrico Pajer | Quantum field theory in curved spacetime with application to problems in gravity and cosmology. Open quantum systems. | Yes |
| Harvey Reall | General Relativity | No |
| Jorge Santos | Exploring diverse facets of general relativity, quantum gravity, gravitational aspects of string theory, and numerical relativity, with a particular focus on studying black holes with anti-de Sitter asymptotics through field theory considerations. | No |
| David Skinner | Twistors, Topological Strings, Quantum Integrability, Celestial Holography | Maybe (PhD students only) |
| David Stuart | PDEs and Analysis of classical and quantum field theories. | No |
| Christopher Thomas | Lattice QCD and hadron physics | Yes |
| David Tong | Quantum field theory | No |
| Maria Ubiali | Quantum Chromo Dynamics, Machine Learning applications in particle physics phenomenology, Standard Model Phenomenology, Parton Distribution Functions, Standard Model Effective Field Theories, Axions, Heavy quarks | Yes |
| Matthew Wingate | Lattice field theory: hadronic matrix elements and weak interactions; novel classical and quantum algorithms | Yes |
General Relativity and Cosmology
| Supervisor | Interests | Taking students for 2027 |
|---|---|---|
| Anthony Challinor | Constraining fundamental cosmology with the CMB and large-scale structure | Yes |
| Miles Cranmer | Machine learning for cosmology and cosmological data analysis, large-scale structure, large numerical simulations, simulation-based inference | Yes |
| Mihalis Dafermos | Proving theorems about general relativity, especially black holes and singularities | Unlikely |
| Maciej Dunajski | Mathematical Physics, Twistor Theory, General Relativity, Solitons | Yes |
| James Fergusson | Higher-order correlation functions of cosmological data sets | No |
| Steven Gratton | Early Universe theory, advanced CMB analysis methods | No |
| Sean Hartnoll | Holographic emergence of space and time in quantum gravity | Maybe |
| Enrico Pajer | Quantum field theory in curved spacetime with application to problems in gravity and cosmology. Open quantum systems. | Yes |
| Harvey Reall | General Relativity |
No |
| Jorge Santos | Exploring diverse facets of general relativity, quantum gravity, gravitational aspects of string theory, and numerical relativity, with a particular focus on studying black holes with anti-de Sitter asymptotics through field theory considerations. | No |
| Paul Shellard | PhD student to work on the Simons Observatory project: analysis of the bispectrum. Student will be co-supervised with Professor James Fergusson | Yes |
| Blake Sherwin | Constraining fundamental physics with CMB and large-scale structure | Possibly |
| Ulrich Sperhake | Modeling of compact objects in the framework of general relativity using numerical techniques, including the calculation of gravitational-wave signatures from these scenarios. | Yes |
| Rita Teixeira da Costa | Partial Differential Equations, General Relativity | Unlikely |
| Claude Warnick | General relativity, classical field theories, analysis of partial differential equations | Probably |
| Zoe Wyatt | General relativity, theoretical cosmology, analysis of partial differential equations | Yes |