02 December 2025

PhD Position: Machine Learning for Fusion Energy Plasma Turbulence

I am looking for a PhD student to work on an exciting project at the intersection of machine learning, computational physics, and fusion energy research.

Project:

Machine Learning for Multi-Fidelity Turbulent Transport Modelling in Tokamak Plasmas

Predicting turbulence and transport in magnetically confined plasmas is one of the major challenges in developing fusion energy. High-fidelity simulations based on nonlinear gyrokinetic theory can accurately model turbulent behaviour in tokamaks but are extremely computationally expensive, often requiring hundreds of thousands of CPU-hours for a single run.

To make large-scale predictive modelling feasible, researchers use simplified, lower-fidelity models such as linear gyrokinetics or gyro-fluid approximations, but these come at the cost of reduced accuracy.

What You’ll Do

This PhD project will explore how machine learning can bridge these fidelity levels, combining the accuracy of high-fidelity models with the efficiency of reduced ones. The work will involve:

Comparing and integrating different turbulence models to identify where simplified approaches remain valid
Developing machine learning-based correction models to improve predictions from lower-fidelity simulations
Investigating whether machine learning can reconstruct high-resolution plasma behaviour from lower-resolution simulations, guided by physical insight into nonlinear interactions
Exploring how recent advances in large-scale deep generative modelling, including diffusion-based and autoregressive architectures, can be adapted to accelerate PDE solvers and plasma turbulence simulations

Prerequisites

Background in physics, applied mathematics, or computer science
Experience and enthusiasm for using deep learning software such as PyTorch (or equivalent frameworks) are essential

Supervision and Funding

Supervisors: Francesco Tudisco (University of Edinburgh), Bhavin Patel (UKAEA, Oxford)
Duration: 4 years full-time
Full scholarship covering tuition fees and stipend
Students will be based at the Culham Centre for Fusion Energy in Oxfordshire, with regular visits to the University of Edinburgh

How to Apply

Applications should be submitted through the University of Edinburgh website.

Important dates:

Application deadline: 9th January 2025
Interviews for shortlisted candidates: Expected during the week of 19th January 2025

The application portal will ask you to fill in a research proposal (optional). This can be used as an occasion to write about your scientific interests, why the project fits with those, and any ideas you have for things you would like to do within the PhD project. Please keep the content short and crisp.

For informal inquiries, feel free to reach out to me directly

[1] P. Rodriguez-Fernandez et al., Nonlinear gyrokinetic predictions of SPARC burning plasma profiles enabled by surrogate modeling 2022 Nucl. Fusion 62 076036

[2] J. Candy et al., Multiscale-optimized plasma turbulence simulation on petascale architectures. Computers & Fluids 188 (2019): 125-135.

[3] G. Staebler et al., Quasilinear theory and modelling of gyrokinetic turbulent transport in tokamaks. Nuclear Fusion 64.10 (2024): 103001.

[4] C. Bourdelle et al., A new gyrokinetic quasilinear transport model applied to particle transport in tokamak plasmas. Physics of Plasmas 14.11 (2007).

[5] W. A. Hornsby et al., Gaussian process regression models for the properties of micro-tearing modes in spherical tokamaks. Physics of Plasmas 31.1 (2024).

[6] V. Gopakumar et al., Plasma surrogate modelling using Fourier neural operators. Nuclear Fusion 64.5 (2024): 056025.

[7] L. Zanisi et al., Efficient training sets for surrogate models of tokamak turbulence with active deep ensembles. Nuclear Fusion 64.3 (2024): 036022.

[8] H. Wang et al, Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey, arxiv:2408.12171

[9] T. Li et al, Synthetic Lagrangian turbulence by generative diffusion models, Nature Machine Intelligence 2024

[10] I. Price et al, Probabilistic weather forecasting with machine learning, Nature 2025

[11] H.A. Majid et al, Test-Time Control Over Accuracy-Cost Trade-Offs in Neural Physics Simulators via Recurrent Depth, NeurIPS 2025

[12] H.A. Majid et al, Solaris: A Foundation Model for the Sun, NeurIPS 2024

Tags: PhD machine learning fusion energy plasma physics turbulence UKAEA