Fully Funded PhD in Data-Efficient and Transferable Machine Learning for Catastrophic Risk Assessment in Offshore Wind Infrastructure University of Surrey in United Kingdom

University of Surrey is advertising a fully funded PhD studentship in machine learning, AI, structural engineering, and offshore wind infrastructure. The project is titled “Data-efficient and transferable machine learning-based predictive models for catastrophic risk assessment in offshore wind infrastructure” and is supervised by Dr Tanmoy Chatterjee and Prof Subhamoy Bhattacharya.

This industry-collaborative project sits at the intersection of computer science, civil engineering, mechanical engineering, and statistics, with a strong focus on physics-informed machine learning, predictive analytics, risk and reliability, digital twins, and multi-fidelity modelling. The research aims to build data-efficient and transferable models for catastrophic risk prediction in offshore wind systems operating under harsh marine and multi-hazard conditions.

Funding is provided by EPSRC, UKRI, and Renew Risk Ltd. The studentship covers tuition fees, includes a UKRI standard stipend, and provides a Research Training Support Grant (RTSG) of £7,500. The duration is 3 years on an accelerated route.

Applicants should have at least a 2:1 Bachelor’s degree or equivalent in AI/ML for Engineering, Structural Engineering, Risk Assessment, or a closely related field. Strong Python and MATLAB skills are essential. Experience in data science, finite-element modelling, structural analysis, predictive analytics, or offshore structures is desirable. The opportunity is open to candidates who pay UK/home rate fees.

The application deadline is 12 July 2026, and the start date is 1 October 2026. To apply, submit your application via the Civil and Environmental Engineering PhD programme page and upload a document stating the project title and relevant supervisor name instead of a research proposal.