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

PhD opportunity at the University of Surrey in data-efficient and transferable machine learning-based predictive models for catastrophic risk assessment in offshore wind infrastructure.

This industry-collaborative project sits at the intersection of machine learning, structural engineering, renewable energy, digital twins, uncertainty quantification, and risk assessment. The research aims to develop trustworthy predictive models for offshore wind infrastructure operating under extreme and uncertain conditions, with a focus on catastrophic risk prediction, fragility assessment, reliability analysis, and resilient infrastructure.

The project will explore physics-informed and data-efficient machine learning, transfer learning and generalisable AI models, multi-hazard fragility assessment, uncertainty-aware multi-fidelity modelling, and the use of unlabelled operational data and self-supervised representation learning. It also offers opportunities to work with real offshore wind farm models and industrial datasets through collaboration with Renew Risk Ltd.

Funding: Fully funded 3-year accelerated studentship. Tuition fees are covered, and a UKRI standard stipend is provided. A Research Training Support Grant of £7,500 is also available for the project term.

Eligibility: 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, predictive analytics, finite-element modelling, structural analysis, or offshore structures is desirable. The opportunity is open to candidates paying UK/home rate fees.

Supervisors: Dr Tanmoy Chatterjee and Prof Subhamoy Bhattacharya at the University of Surrey.

Deadline: 12 July 2026. Start date: 1 October 2026.

How to apply: Apply through the Civil and Environmental Engineering PhD programme page. Instead of a research proposal, upload a document stating the project title and the relevant supervisor name.