PhD Studentship: Cohesive Powder Flow in Confined Spaces

As the UK accelerates its ambition to establish a secure, resilient supply of High-Assay Low Enriched Uranium (HALEU) for next-generation reactors, a new and urgent scientific challenge has emerged. When traditional deconversion processes, transforming uranium hexafluoride (UF₆) into uranium oxide (U₃O₈), are scaled down to meet stricter criticality safety requirements, the behaviour of the resulting powder changes dramatically. These confined conditions lead to unpredictable flow interruptions, problematic material build-up, and inconsistent product quality.

This PhD project, funded by the United Kingdom National Nuclear Laboratory (UKNNL), aims to optimise both process design and material properties, to enable reliable powder handling, and to directly support the development of next generation of advanced nuclear reactors.

The research will adopt a combined experimental, numerical, and machine learning approach to investigate cohesive powder flows in confined spaces, targeting unit operations relevant to HALEU deconversion.

The PhD candidate will work closely with the UKNNL team, gaining direct operational insights into nuclear reactor design and operation. This ensures that the research stays rooted in practical nuclear environments while simultaneously cultivating valuable industry connections.

Supervisor: Professor Charley Wu

Entry requirements

Open to UK nationals only (Home/UK students). Starting in October 2026. Later start dates may be possible, please contact Professor Charley Wu once the deadline passes.

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

Applicants must have (or be on track to obtain) a first-class or upper second-class honours degree (2.1), or a distinction/merit at master's level, in engineering, physics or a related field.

We are look for a curious and motivated candidate who is comfortable working across academic and industrial environments. Experience with, or a strong interest in, one or more of the following areas is particularly welcome:
• AI and machine learning
• Computational modelling (DEM, CFD, or FEM)
• Particle technology

How to apply

Applications should be submitted via the 'Apply' button above. 

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.

Funding

This fully funded studentship is available from 1 October 2026 for a period of three and a half years, subject to satisfactory progress.
The studentship includes a tax-exempt stipend, currently set at £21,805 per annum (2026/27 rate). In addition, full-time tuition fees will be covered for up to three and a half years (Home/UK students only).

Application deadline

22 June 2026

Enquiries

Contact Professor Charley Wu

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