Early-stage failure prediction in fusion materials using machine learning

University of Sheffield; United Kingdom; On-site; GBP 19,000 - 23,000; Full time

Job summary

The University of Sheffield offers a fully funded PhD project focusing on the application of machine learning and pattern recognition to identify early-stage deformation patterns in materials used in nuclear fusion reactors. This project is part of the EPSRC CDT in Materials 4.0 and aims to enhance safety in fusion reactor operations.

Benefits

Fully funded studentship

Tax-free stipend

Research training support grant

Qualifications

• Experience in pattern recognition and machine learning techniques.
• Background in materials science or nuclear engineering is advantageous.

Responsibilities

• Apply machine learning techniques to analyze experimental data.
• Identify early-stage deformation patterns in materials.

Skills

Pattern recognition

Machine learning

Education

PhD in relevant field

Job description

In fusion reactors, materials experience extreme temperatures, stresses, and radiation damage. Safe operation requires identification of deformation patterns that are early warning signs of materials failure. These characteristic patterns result from the interaction of deformation mechanisms across multiple scales making detection via traditional analytical methods extremely challenging. This project will apply pattern recognition and machine learning techniques to a large database of experimental data to reveal early-stage fingerprints for damage hidden in the data.

Project Description:

In nuclear fusion reactors, particularly plasma-facing first wall components and breeder blanket modules, materials are subjected to extreme temperatures, stresses, and radiation damage during their operating conditions. Critical to the safe design and operation of a fusion reactor is the early-stage identification of deformation patterns that is a consistent precursor to material failure.

Supervisor:

1. Chris Race

Application Deadline:

16 May 2025

Funding Notes:

This is a fully funded project, part of cohort 2 of the EPSRC CDT in Materials 4.0. CDT. The studentship covers fees, a tax-free stipend of at least £19,237 plus London allowance if applicable, and a research training support grant.

The Materials 4.0 CDT is committed to Equality, Diversity and Inclusion. Five countries are represented in cohort 1. We would like to see a more gender-balanced cohort 2, so we strongly encourage applications from female candidates.

Enquiries:

For general enquiries, please contact [e-mail].

For application-related queries, please contact Sharon Brown ([e-mail]). Please note that each partner of the CDT in Materials 4.0 will have its own application process.

If you have specific technical or scientific queries about this PhD, we encourage you to contact the lead supervisor, Chris Race ([e-mail]).

Application Webpage:

https://www.sheffield.ac.uk/postgradapplication/login.do

After the personal details, you need to 'add research course', and select 'Doctoral Training Course', and then 'Developing National Capability for Materials 4.0'.