Tanmoy Chatterjee
6 months ago
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Fully Funded PhD in Data-Efficient and Transferable Machine Learning for Catastrophic Risk Assessment in Offshore Wind Infrastructure University of Surrey in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded studentship for 3 years (accelerated route). Funding includes UKRI standard stipend, tuition fees covered, and a Research Training Support Grant (RTSG) of £7,500 for the project term. The project is funded by EPSRC, UKRI, and Renew Risk Ltd.
Deadline
Expired
Country
United Kingdom
University
University of Surrey
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About this position
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.
Funding details
Fully funded studentship for 3 years (accelerated route). Funding includes UKRI standard stipend, tuition fees covered, and a Research Training Support Grant (RTSG) of £7,500 for the project term. The project is funded by EPSRC, UKRI, and Renew Risk Ltd.
What's required
Applicants must meet the minimum entry requirements for the PhD programme and hold 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 programming skills in Python and MATLAB are essential. Experience in data science, predictive analytics, finite-element modelling, structural analysis, and/or offshore structures is desirable. Candidates should have strong analytical and problem-solving skills, excellent written and verbal communication, and the ability to work independently and adapt quickly to new methods and technologies. The studentship is open to candidates who pay UK/home rate fees and are subject to eligibility criteria.
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Professors
Lecturer in Resilient Design
United Kingdom
Research Interests
Statistics
Structural Engineering
Digital Twin Technology
Machine Learning
Computer Science
Professor
United Kingdom
Research Interests
Geotechnical Engineering
Offshore Engineering
Geomechanics
Civil Engineering
Wind Engineering
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.