PhD Studentship: A new hybrid physics-AI platform for chemometrics in the nuclear decommissioning sector

This project aims to advance chemometrics, artificial intelligence (AI), and data analytics techniques for characterisation of materials in decommissioning nuclear sites to enhance detection of radiological and chemical contamination and activity assessments. We propose development of an advanced data analytics platform for spectroscopic data that combines state-of-the-art existing statistical techniques for multivariate curve resolution, together with physics-informed AI to overcome Nuclear Decommissioning Authority (NDA) data analysis challenges. Previous machine learning (ML) uses ‘out-the-box’ algorithms, however these may not provide physically interpretable results or quantifiable uncertainty. We propose developing data pipelines combining advanced preprocessing techniques, statistical tools, and ML that incorporates physics, reducing noise via preprocessing and corrections, and combined with uncertainty quantification for robustness.

Working closely with NDA we will develop algorithms, providing the PhD researcher advanced training, international research experience, and soft-skills to enable them to become a research expert. We are seeking enthusiastic and motivated applicants with an interest in data science, computational chemistry or related areas. A degree in Chemical Engineering, Chemistry, Computer Science, Mathematics or other engineering/science disciplines with significant computational elements, and some coding experience in a programming language (e.g., Python, MATLAB, Julia) are essential.

The successful candidate will be supervised by:

Dr Michael Short (https://www.surrey.ac.uk/people/michael-short),

Dr Monica Felipe-Sotelo (https://www.surrey.ac.uk/people/monica-felipe-sotelo),

Dr Carol Crean (https://www.surrey.ac.uk/people/carol-crean), and

Dr Jeremy Andrew (NRS Dounraey),

and based in the School of Chemistry and Chemical Engineering.

We have a long history of excellence in computational chemistry research. We have a vibrant, interdisciplinary group of researchers working in a variety of areas to solve global problems in analytical chemistry and data science, creating a positive, supportive research culture. Candidates will be expected to go on secondments at NDA facilities during the PhD. Funding is subject to final agreement between NDA, UKNNL, Surrey and the candidate.

https://www.surrey.ac.uk/school-chemistry-and-chemical-engineering

https://uknnl.com/

https://www.gov.uk/government/organisations/nuclear-decommissioning-authority

Supervisors: Dr Michael Short, Dr Monica Felipe-Sotelo, Dr Carol Crean and Dr Jeremy Andrew

Entry requirements

Open to UK nationals only. Starting in October 2025. Later start dates may be possible, please contact Dr Michael Short once the deadline passes.

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

Applicants are expected to hold a first or upper-second class degree in a relevant discipline (or equivalent overseas qualification), or a lower second plus a good Masters degree (distinction normally required).

Open to any UK candidates. We are seeking candidates with:

• Relevant subject matter experience* (e.g. data science, chemistry, computational chemistry, chemical engineering, etc.)
• Willingness to adapt and work in different disciplines
• Ability to work independently and cooperatively
• Commitment to inclusivity, responsible research and innovation

How to apply

Applications should be submitted by clicking 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

Fully and directly funded for this project only. Full tuition fee waiver p.a. (Home Students only) and stipend at above UKRI rates p.a. (currently at £20,780 for 2025/26 academic year, increasing in line with inflation).  Funding is available for 4 years.

Application deadline: 6 July 2025

Enquiries: Contact Dr Michel Short

Ref: PGR-2425-055

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