PhD projects for 2027

Earth system digital twins fuse machine learning models with real-world observations to predict the impact of policy choices on the environment and society under different scenarios.

This project involves working at the intersection of applied mathematics, machine learning and climate science to develop next-generation digital twins that capture uncertainties in complex physical models, advancing these powerful tools for better-informed decision-making in a changing climate.

Supervisors

• Amos Lawless (University of Reading and National Centre for Earth Observation, NCEO)
• Rossella Arcucci (Imperial College)
• Jennifer Scott (University of Reading).

Partner

• National Centre for Earth Observation.

UK water companies have a regulatory requirement to protect against severe drought, defined as a 1-in-200-year event.

This project will develop defensible methods of testing this requirement using physical climate storylines (physically self-consistent, causal explanations) to construct plausible worst cases, anchored in downward counterfactuals of historical events.

Supervisors

• Ted Shepherd (University of Reading)
• Geoff Darch (Anglian Water)
• Kate Marvel (NASA Goddard Institute for Space Studies), USA
• Ed Hawkins (University of Reading).

Partner

• Anglian Water.

The atmosphere is changing in response to global warming. This project will develop a model for the evolution of the global climate state in response to forcing using the mathematical theory of optimal transport and investigate the key question of stability of those states using Hamiltonian dynamics.

Supervisors

• John Methven (University of Reading)
• Charlie Egan (University of Reading)
• Darryl Holm (Imperial College London).

Sting jets are airstreams within intense winter storms that can lead to damaging surface wind gusts. Detecting them in gridded numerical output, for example from climate models, is currently computationally expensive.

This project will investigate climate change effects in sting-jet occurrence through the development of AI-based sting-jet detection methods.

Supervisors

• Oscar Martínez-Alvarado (University of Reading/National Centre for Atmospheric Science, NCAS)
• Kieran Hunt (University of Reading/NCAS)
• Ben Harvey (University of Reading/NCAS)

Partner

• National Centre for Atmospheric Science.

Climate forecasts underpin critical decisions, yet evaluating their performance is statistically challenging because climate systems are non-stationary and analytical choices can strongly influence conclusions.

This project will develop methods to assess the robustness and reproducibility of forecast evaluations, distinguishing unavoidable forecasting uncertainty from variability introduced by alternative, defensible statistical analyses.

Supervisors

• Etienne Roesch (University of Reading)
• Ted Shepherd (University of Reading).

Use mathematics to better understand the ocean. In this project, you will develop numerical methods to combine satellite measurements of sea surface height with dynamical ocean models, improving estimates of the ocean state.

After initially working on idealised systems, in collaboration with European Centre for Medium-Range Weather Forecasts (ECMWF) scientists, you will work on real data and contribute to important advances in weather prediction and climate monitoring.

Supervisors

• Amos Lawless (University of Reading and National Centre for Earth Observation
• Jennifer Scott (University of Reading)
• Phil Browne (ECMWF).

Partners

• European Centre for Medium-Range Weather Forecasts
• National Centre for Earth Observation.

This PhD tackles one of the biggest computational challenges in climate science: how to combine vast datasets and models efficiently.

By developing new mixed-precision algorithms, you’ll explore how reduced numerical accuracy can unlock faster, greener weather and climate prediction – working at the frontier of mathematics, computing, and Earth system science.

Supervisors

• Jennifer Scott (University of Reading)
• Sarah Dance (University of Reading and National Centre for Earth Observation).

Partner

• National Centre for Earth Observation.

Help shape the future of low-carbon energy. This PhD will explore how district heat networks can support the transition to decarbonised heating by easing pressure on power systems.

You will develop statistical models of heat demand and dynamic network models, using sensitivity and uncertainty analysis to identify smart, time-dependent demand-side management strategies.

Supervisors

• Mehdi Shahrestani (University of Reading)
• Stefán Thor Smith (University of Reading)
• William Holderbaum (University of Reading).

Partners

• Reading Borough Council
• Royal Berkshire Hospital Estates Team.

Machine learning (ML) is a transformative force in weather prediction, but the same methods cannot be used for climate because they cannot learn from a climate that hasn’t happened yet.

This project will create a new hybrid ML/physical model, learning the detail that is too expensive for a physical model, while keeping the large-scale laws of the physical model, such as energy conservation.

Supervisors

• Hilary Weller (University of Reading)
• Dan Shipley (University of Reading)
• Kieran Hunt (University of Reading)
• James Kent (UK Met Office)
• Christian Kuehnlein (European Centre for Medium-Range Weather Forecasts).

Partners

• UK Met Office
• European Centre for Medium-Range Weather Forecasts.

Climate change is having adverse effects on our ability to supply food. In research at the forefront of supply chain dynamics we will use mathematical modelling to improve the resilience of UK fresh food supply chains in the context of future heat shocks in the UK and Europe.

Supervisors

• Marcus Tindall (University of Reading)
• Zuowei Wang (University of Reading).

Tropical cyclones are among the costliest weather systems. This PhD aims to build physics-informed AI to improve the physical consistency of global tropical cyclone prediction from monthly to decadal timescales.

Explore new drivers of predictability, develop probabilistic forecasts with quantified uncertainty, and engage national forecasting agencies and industry on societal and economic impacts.

Supervisors

• Xiangbo Feng (University of Reading)
• Rémi Tailleux (University of Reading)
• Ralf Toumi (Imperial College London).

Develop new rigorous inverse ocean models, deriving absolute circulation, eddy-induced transports and turbulent mixing coefficients, from observations.

Using dynamic and thermodynamic conservation laws, you will investigate identifiability, conditioning and well-posedness, seeking five-parameter descriptions of ocean stirring and mixing that can be used to improve understanding of the current ocean circulation state.

Supervisors

• Rémi Tailleux (University of Reading)
• Keith Haines (University of Reading)
• David Ferreira (University of Reading).

This PhD will develop an individual-based bioenergetic model to predict how environmental variability affects Asian seabass growth, energy allocation and resilience in integrated multitrophic aquaculture.

Combining selectively bred fish and environmental data from Thailand with mechanistic modelling, the project will provide a predictive framework for optimising production under changing environmental conditions.

Supervisors

• Shovonlal Roy (University of Reading)
• Hong Yang (University of Reading)
• Robert Thorpe (Centre for Environment, Fisheries and Aquaculture Science, CEFAS)
• Joseph Watson (CEFAS).

Partner

• Centre for Environment, Fisheries and Aquaculture Science.