Data-Driven Numerical Modelling of Materials for High-Temperature Nuclear and Energy Applications

About the Project

Next-generation nuclear and fusion energy systems place unprecedented demands on structural and functional materials, which must operate reliably under extreme thermal, mechanical, and environmental conditions. Accurate prediction of material behaviour and degradation is essential for safe design, long-term operation, and risk reduction in such critical infrastructure.

This PhD project will develop a novel data-driven framework for discovering interpretable material models applicable to high-temperature and high-stress environments. Working in collaboration with an industrial partner, the student will focus on materials of strategic relevance—such as metals, ceramics, alloys, or concrete—selected to reflect real-world engineering priorities.

The research will integrate advanced full-field imaging techniques, including X-ray computed tomography, neutron tomography, and related methods, with modern machine-learning approaches such as sparse regression and physics-informed learning. The objective is to extract physically grounded, transparent models from complex datasets, bridging experimental observation and predictive simulation.

This PhD provides high-level training at the intersection of materials science, computational mechanics, and data-driven modelling, preparing graduates for careers in nuclear engineering, advanced energy systems, and high-impact research and industrial environments worldwide.

The project is embedded within a research group with established collaborations with major industrial partners in the nuclear sector, including EDF Energy, Sellafield and the National Nuclear Laboratory. These collaborations ground the research in real engineering challenges in the nuclear sector, while preserving a strong fundamental focus.

Eligibility Requirements

We welcome applications from candidates worldwide. Applicants should have:

• 1st or 2:1 degree in Engineering, Materials Science, Physics, Chemistry, Applied Mathematics, or other Relevant Discipline.
• Experience in numerical modelling/materials.
• Strong mathematics, physics, and computer programming skills.

If English is not your first language, you may be required to provide evidence of English language proficiency (e.g. IELTS or TOEFL), in accordance with the University of Sheffield requirements.

Research group, collaborations and contact:

The project is embedded within a vibrant and internationally recognised research group, with established collaborations with major industrial partners—EDF Energy, Unipart Construction Technology, and Sellafield Ltd—as well as European academic partners. These collaborations ground the research in real engineering challenges across the energy, nuclear, and infrastructure sectors, while retaining a strong fundamental research focus.

For more details please contact Dr Giacomo Torelli within the School of Mechanical, Aerospace and Civil Engineering at g.torelli@sheffield.ac.uk

Funding Notes

This project is offered on a self-funded or externally funded basis. Applicants must demonstrate access to suitable financial support from personal, national, or institutional sources to cover tuition fees and living expenses. Unfortunately, university funding is not available for this project at this time.