Real-time Predictive Monitoring and Control of Nuclear Fuel Manufacturing Using Digital Twins Lancaster University in United Kingdom

Applications are invited for a fully funded PhD studentship in the School of Engineering at Lancaster University, supervised by Professor James Taylor and colleagues. This project, jointly funded by the EPSRC and an industrial partner, focuses on the application of machine learning and digital twin technologies to the nuclear fuel cycle, specifically targeting an innovative, integrated dry-route uranium conversion process. The approach aims to reduce environmental impact and production costs by eliminating liquid water from the process, moving beyond conventional wet methods.

The PhD researcher will develop advanced data-driven modelling and analysis techniques to enable real-time monitoring, adaptive control, predictive maintenance, and asset management in an industrial environment. The project will leverage richer datasets to construct a high-fidelity digital twin of the manufacturing process, with the goal of improving process performance and product quality. Early studies have shown that machine learning models can predict uranium dioxide output quality from process signals, and this project will significantly expand on these findings.

Students will gain hands-on expertise in data science, machine learning for complex physical systems, digital twin development and validation, nuclear fuel cycle fundamentals, predictive modelling, and uncertainty quantification. The project includes direct exposure to industrial deployment through collaboration with the industrial partner, as well as structured university training in nuclear engineering, computational and statistical methods, project management, and scientific writing. On-site training, secondments, and close industry engagement are integral to the experience.

The studentship is open to graduates in Engineering, Physics, Mathematics, or closely related STEM disciplines. The project is funded by an IDLA studentship (formerly iCASE) for 4 years, providing a tax-free stipend at UKRI rates and university fees at the home (UK) rate. Funding also covers travel to the industry partner and conferences/workshops. Non-UK students may be eligible for higher fee rates and should discuss this with Professor Taylor.

Applications are accepted year-round. Interested candidates should send a CV and personal statement/covering letter addressing their background and suitability for the project to Professor James Taylor ([email protected]). Informal enquiries are welcome. For more information, visit the project page or the FindAPhD listing.