Publisher
source

Tanmoy Chatterjee

6 months ago

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 flag

Country

United Kingdom

University

University of Surrey

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Keywords

Computer Science
Machine Learning
Mechanical Engineering
Risk Assessment
Artificial Intelligence
Civil Engineering
Structural Engineering
Reliability Engineering
Digital Twin Technology
Predictive Analytics
Data-driven Modeling
Statistics

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

University Name
Country flag

United Kingdom

Research Interests

Statistics

Structural Engineering

Digital Twin Technology

Machine Learning

Computer Science

Professor

University Name
Country flag

United Kingdom

Research Interests

Geotechnical Engineering

Offshore Engineering

Geomechanics

Civil Engineering

Wind Engineering