Before applying to the DAMTP PhD you are encouraged to discuss informally with possible supervisors. 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 CV and brief explanation of your areas of interest.
Applied and Computational Analysis (including CMI)
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 (including CMI)
| Supervisor | Interests | Taking students for 2026 |
|---|---|---|
| Angelica Aviles-Rivero | Inverse problems, machine learning, computation mathematics | Yes |
| Natalia Berloff | Physics of information, physical neural networks, coherent quantum systems and physics-inspired computing | Yes |
| Colm-cille Caulfield | Data-driven methods and high performance computing for climate and environmental science | Yes |
| Matthew Colbrook | Foundations of AI, data-driven dynamical systems, spectral problems, PDEs, optimisation | Yes |
| Nilanjana Datta | Quantum information theory, in particular, quantum (and classical) entropies and divergences, entanglement theory, quantum channels, hypothesis testing | Possibly |
| 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 |
| 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 | Numerical approaches to non-Newtonian fluid-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 engineering, geophysical and astrophysical contexts; Non-Newtonian particularly polymer flows (drag reduction, elasto-inertial and elastic turbulence). | Yes |
| Chao Li | Developing artificial intelligence and multi-omics approaches to better understand brain structure/function and brain disorders | Yes |
| Paul Linden | ||
| 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 |
| Alexei Shadrin | ||
| 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 |
Observational analyses and analytical/numerical approaches to fluid-dynamics problems in Earth and planetary science |
Yes |
Astrophysics
Information for PhD applicants intending to apply to this area is available through this link.
| Supervisor | Interests | Taking students for 2026 |
|---|---|---|
| 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 | 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 | Yes |
| Nicole Shibley | Planetary science; ice-ocean interactions in Earth and planetary environments, such as ice-covered moons in the Solar System; habitability | 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 2026 |
|---|---|---|
| David Abrahams | ||
| 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 computing | Yes |
| Rajesh Bhagat | Interfacial flows, building ventilation flow, airborne disease transmission | Yes |
| Michael Cates | Statistical physics of soft and active matter | Unlikely |
| Colm-cille Caulfield | Stratified turbulence and mixing, generalized stability theory, environmental and industrial fluid dynamics |
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 |
| 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 | Unlikely |
| 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 |
| 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
|
| Peter Haynes | ||
| Duncan Hewitt | Non-Newtonian fluids, geological and industrial flows, clogging and jamming flows, porous media | Yes |
| Robert Jack | Statistical physics, rare events, soft matter, glassy dynamics and metastability | Yes |
| Rich Kerswell | General stability theory, transition and turbulence in engineering, geophysical and astrophysical contexts; Non-Newtonian particularly polymer flows (drag reduction, elasto-inertial and elastic turbulence). | Yes |
| Henrik Latter | Instabilities, waves, and turbulence in astrophysical settings; Protoplanetary disks and planet formation | 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 |
| Paul Linden | ||
| 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 | Yes |
| Jerome Neufeld | ||
| 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 | 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 |
| Grae Worster | Coupling of fluid mechanics and solidification in geophysical contexts, and the dynamics of hydrogels | Possibly |
Machine Learning (fundamentals and applications to healthcare)
| Supervisor | Interests | Taking students for 2026 |
|---|---|---|
| Natalia Berloff | Physics of information, physical neural networks, coherent quantum systems and physics-inspired computing | Yes |
| Miles Cranmer | Machine learning for physical sciences, ML-accelerated simulation, foundation models for science (polymathic-ai.org/), AI for scientific discovery | Yes |
| Chao Li | Developing artificial intelligence and multi-omics approaches to better understand brain structure/function and brain disorders | Yes |
| Carola Schonlieb | Mathematical imaging, inverse problems, optimisation, mathematical foundations of machine learning | 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 2026 |
|---|---|---|
| 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 computing | Yes |
| Angela Capel Cuevas | Quantum information theory, quantum many-body systems, entropic inequalities, quantum Markov semigroups, correlations on thermal states and algorithms for quantum many-body systems | Yes |
| Nilanjana Datta | Quantum information theory, in particular, quantum (and classical) entropies and divergences, entanglement theory, quantum channels, hypothesis testing | Possibly |
| Hamza Fawzi | Convex optimization and applications to quantum information theory, quantum many-body theory; Semidefinite programming relaxations and sums of squares | Possibly |
| Boris Groisman | ||
| Adrian Kent | Quantum information theory and applications, quantum foundations, quantum theory and gravity, experimental tests | Possibly |
| Sergii Strelchuk | Quantum computing, quantum algorithms, quantum channels, quantum simulation | No |
| Frank Verstraete | Quantum entanglement and its role in many-body physics | Yes |
Mathematical Biology
| Supervisor | Interests | Taking students for 2026 |
|---|---|---|
| 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 |
| 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 |
| Chao Li | Developing artificial intelligence and multi-omics approaches to better understand brain structure/function and brain disorders | Yes |
| Gos Micklem | Computational biology | Possibly |
| Carola Schonlieb | Mathematical imaging, inverse problems, optimisation, mathematical foundations of machine learning | Yes |
| Ben Simons | ||
| Mihaela van der Schaar |
Machine Learning and Artificial Intelligence |
Yes |
High Energy Physics
| Supervisor | Interests | Taking students for 2026 |
|---|---|---|
| Ben Allanach | HEP phenomenology | No |
| Alejandra Castro | String Theory, AdS/CFT, and Quantum Gravity | Yes |
| 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 | Yes/Possibly |
| Sven Krippendorf | Machine Learning for Theoretical Particle Physics and Cosmology, Physics of neural networks | Yes |
| Alex Mitov | Unlikely | |
| Enrico Pajer | Quantum field theory in curved spacetime and theoretical cosmology | Yes |
| Harvey Reall | General Relativity | Yes |
| 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 | Yes |
| David Skinner | Celestial Holography, Topological String Theory, Twistor Theory | No |
| David Stuart | Analysis and Mathematical physics | No |
| Christopher Thomas | Lattice QCD and hadron physics | Yes |
| David Tong | Quantum field theory | Yes |
| 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 |
| Aron Wall | Holographic quantum gravity, black hole thermodynamics, T^2 deformation | Possibly |
| 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 2026 |
|---|---|---|
| Anthony Challinor | Constraining fundamental cosmology with the CMB and large-scale structure | Probably |
| William Coulton | CMB theory and data analysis, particularly methods to isolate different sky signals and characterize CMB secondaries, and primordial non-Gaussianity, both in the CMB and in large scale structure | Possibly |
| 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 | Unlikely |
| Steven Gratton | Early Universe theory, advanced CMB analysis methods | Possibly |
| Sean Hartnoll | Holographic emergence of space and time in quantum gravity | Yes/Possibly |
| Christopher Moore | Possibly | |
| Enrico Pajer | Quantum field theory in curved spacetime and theoretical cosmology | Yes |
| Harvey Reall | General Relativity |
Yes |
| 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 | Yes |
| Paul Shellard | Possibly | |
| Blake Sherwin | Constraining fundamental physics with CMB and large-scale structure | Possibly |
| Ulrich Sperhake | Gravitational wave physics, numerical relativity | Unlikely |
| Rita Teixeira da Costa | Partial Differential Equations, General Relativity | Possibly |
| Aron Wall | Holographic quantum gravity, black hole thermodynamics, T^2 deformation | Possibly |
| Claude Warnick | General relativity, classical field theories, analysis of partial differential equations | Possibly |