integrity management, enabling the extension of lifetimes and the reduction of maintenance costs.

This project addresses these challenges by combining experimental breakthroughs with advanced machine learning (ML) to transform how structural integrity is assessed and predicted. While ML holds transformative potential, challenges such as resistance to new methods and the reliance on high-quality datasets must be overcome. Partnering with the National Physical Laboratory ensures access to critical datasets, cutting-edge facilities, and industrial validation.

This collaboration enhances data reliability and fosters confidence in ML-powered solutions. By delivering robust, scalable, and transferable approaches, this project advances structural integrity management, supports the UK’s fusion energy ambitions, and provides innovative tools for sustainable technologies across engineering sectors.

Supervisors: Dr Tan Sui, Professor Mark Whiting and Dr Tony Fry

Entry requirements

Open to candidates who pay UK/home rate fees. Starting in October 2026. Later start dates may be possible, please contact Dr Tan Sui once the deadline passes.

You will need to meet the minimum entry requirements for our PhD programme.

Candidate Profile

Applicants should have (or expect to obtain by the start date) at least an Upper Second Bachelor’s degree, and preferably a Master’s degree, in an appropriate discipline (e.g. engineering, material sciences, mechanical engineering, physics, chemistry or a related subject)

How to Apply

Applications should be submitted via the Engineering Materials PhD programme page. In place of a research proposal, you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.

integrity management, enabling the extension of lifetimes and the reduction of maintenance costs.

This project addresses these challenges by combining experimental breakthroughs with advanced machine learning (ML) to transform how structural integrity is assessed and predicted. While ML holds transformative potential, challenges such as resistance to new methods and the reliance on high-quality datasets must be overcome. Partnering with the National Physical Laboratory ensures access to critical datasets, cutting-edge facilities, and industrial validation.

This collaboration enhances data reliability and fosters confidence in ML-powered solutions. By delivering robust, scalable, and transferable approaches, this project advances structural integrity management, supports the UK’s fusion energy ambitions, and provides innovative tools for sustainable technologies across engineering sectors.

Supervisors: Dr Tan Sui, Professor Mark Whiting and Dr Tony Fry

Entry requirements

Open to candidates who pay UK/home rate fees. Starting in October 2026. Later start dates may be possible, please contact Dr Tan Sui once the deadline passes.

You will need to meet the minimum entry requirements for our PhD programme.

Candidate Profile

Applicants should have (or expect to obtain by the start date) at least an Upper Second Bachelor’s degree, and preferably a Master’s degree, in an appropriate discipline (e.g. engineering, material sciences, mechanical engineering, physics, chemistry or a related subject)

How to Apply

Applications should be submitted via the Engineering Materials PhD programme page. In place of a research proposal, you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.