Tanmoy Chatterjee

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.

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.