PhD Studentship: Machine Learning Enhanced Muon Imaging for Nuclear Waste Monitoring and Safeguards

This PhD explores the use of recurrent neural networks (RNNs) to enhance muography (muon-based) monitoring of radioactive waste disposal sites. If the temporal evolution of muon flux and reconstructed density data is known, RNNs can learn the baseline of a systems behaviour. Using this baseline anomalies associated with container movement, material structural degradation, or changes in shielding can be identified. In addition, the project will explore the use of RNNs for noise reduction and predictive monitoring, supporting the reliability of long-term surveillance. When combined with convolutional neural networks (CNNs) for spatial feature extraction, this studentship aims to develop a robust, machine learning–driven framework for the continuous safeguarding of nuclear waste storage facilities.

The project will be carried out in collaboration with an industry partner, Geoptic, a leader in muography technologies. Geoptic will provide access to proprietary datasets and dedicated computational resources to support model development and evaluation. In addition to academic supervision, the student will benefit from industrial co-supervision, ensuring strong alignment with real-world applications and challenges. The PhD will include a 3–6 month placement within Geoptic, offering valuable experience and the opportunity to translate research outcomes into practical deployment.

All students funded through the RAPTOR DFA network will be required to undertake 60 credits of training modules as part of the PhD, which will be carried out during the first 3 months of the PhD. Upon completion of the training modules you will receive a Postgraduate Certificate in Nuclear Skills awarded by the University of Liverpool, in addition to your PhD. During the period October – December 2026 you will be required to spend 1 week at each of our partner DFA Universities at Liverpool, Manchester and Suffolk to attend some of these training lectures. All costs for these visits will be covered by the DFA, and more information about this training will be provided when you apply for the PhD.

The applications are open to candidates who pay UK/home rate fees.

Supervisors: Dr Caroline Shenton-Taylor and Professor Daniel Doherty

Entry requirements

Open to candidates who pay UK/home rate fees. See UKCISA for further information. Starting in October 2026. Later start dates may be possible, please contact Dr Caroline Shenton-Taylor once the deadline passes.

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

How to apply

Applications should be submitted via the Physics PhD programme page. In place of a research proposal, you should upload a document stating that this application is to the RAPTOR Nuclear Skills DFA, the title of the project that you wish to apply for and the name of the relevant supervisor.

Funding

Stipend is fully funded at the enhanced UKRI level of £26,000 per year, for 4 years. UK tuition fees are included in the funding offer. RTSG is £5k per year. Funded by RAPTOR Nuclear Skills Doctoral Focal Award.

Application deadline: 12 July 2026

Enquiries: Contact Dr Caroline Shenton-Taylor

Ref: PGR-2526-076

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