Research studentships at the Department of Engineering Science at the University of Oxford

We will post here any funded doctoral studentships in the Department of Engineering Science. For information on other funding opportunities available at Oxford, see the University's Graduate Admissions 'Fees and funding' webpage.

 

Deadline: Noon on 1 May 2026

Research Studentship in Computer Engineering for AI-Driven Manufacturing

 

Research projects being offered to prospective new postgraduate students

Thermomigration of Hydrogen in Reactor Fuel Cladding Materials (Felix Hofmann & Martínez-Pañeda)

This project will investigate how temperature gradients drive hydrogen diffusion. This little-studied effect, called thermomigration, can strongly impact hydrogen embrittlement. It is especially important in systems with steep thermal gradients, such as heat-exchangers in hydrogen fuel systems, nuclear fuel cladding and fusion reactor armour. This project will develop a new experimental rig to measure thermomigration and use this to construct a machine-learning-enhanced digital material twin to correctly capture the effect. The project is funded by Rolls-Royce and the Materials 4.0 CDT. As such, recruitment will be through the Department of Materials.

For further details see: https://www.materials.ox.ac.uk/article/thermomigration-of-hydrogen-in-reactor-fuel-cladding-materials

Machine Learning and Crystal Plasticity Modelling of Hydrides in Zr-alloy Materials for Nuclear Fuel Cladding (Edmund Tarleton)

This project addresses hydride precipitation and irradiation damage in zirconium alloys for nuclear fuel cladding, where it can lead to cracking and material degradation. Current models rely on simplified assumptions and fail to capture key effects such as microstructure, stress, and irradiation-induced changes. The project will develop high-fidelity, physics-based models incorporating hydride formation and irradiation damage, calibrated against experimental data. These models will then be used to train physics-informed machine learning tools for efficient prediction of material behaviour. The ultimate goal is to create a computationally efficient digital twin capable of predicting cladding integrity under realistic operating conditions. The project is funded by Rolls-Royce and the Materials 4.0 CDT. As such, recruitment will be through the Department of Materials.

For further details see: https://www.materials.ox.ac.uk/article/machine-learning-and-crystal-plasticity-modelling-of-hydrides-in-zr-alloy-materials-for-nucl