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Oliver Buxton

Top university

6 months ago

PhD Studentship: Maximising Performance with Scientific Machine Learning in Offshore Wind Farm Aeronautics Imperial College London in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Expired

Country flag

Country

United Kingdom

University

Imperial College London

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Keywords

Computer Science
Machine Learning
Environmental Science
Mechanical Engineering
Aerospace Engineering
Wind Energy
Power Generation
Cloud Physics
Turbulence
Data Assimilation
Large Eddy Simulation
Physics

About this position

[Full tuition fees and an annual tax-free stipend of £22,780 for Home, EU, and International students.]

This PhD studentship at Imperial College London focuses on maximising the performance of offshore wind farms using scientific machine learning. The project investigates the complex interactions between offshore wind turbines and the Marine Boundary Layer (MBL), particularly how these interactions influence the formation of marine stratocumulus clouds and the resulting impact on wind farm power generation. The research aims to answer key questions about whether turbine-driven changes in the MBL promote or hinder cloud formation, and how these changes affect the mesoclimate and wind farm performance.

The project leverages high-fidelity large eddy simulation (LES) codes and scientific machine learning tools, including real-time optimisers, to simulate wind farms under various atmospheric inflows. Candidates will engage in code development to implement actuator disc/line wind-turbine models, facilitating a deeper understanding of the flow physics surrounding cloud formation. The research will also involve developing machine learning-based strategies for discovering self-similarity laws, quantised local reduced order models, and real data assimilation.

Supervised jointly by Prof. Oliver Buxton (expert in turbulence, wind-energy flows, and cloud microphysics) and Prof. Luca Magri (expert in scientific machine learning for aeronautical applications), the successful candidate will be integrated into two leading research groups at Imperial College London, both of which host ERC projects. Collaboration with a group in The Netherlands is possible, requiring short-term travel.

The studentship is fully funded, covering tuition fees and providing an annual tax-free stipend of £22,780 for Home, EU, and International students. The position is open to candidates with a First class honours MEng/MSci or higher degree (or international equivalent) in Aeronautical/Mechanical Engineering or similar STEM subjects. Applicants should demonstrate a willingness to learn new skills and techniques. The duration of the studentship is 3.5 years, with a start date between 1 October 2026 and 1 July 2027.

Imperial College London is committed to diversity and inclusion, holding the Athena SWAN Silver Award, being a Stonewall Diversity Champion, and a Disability Confident Employer. The institution also partners with GIRES to promote respect for trans people.

To apply, submit your application via the Imperial College London Apply webpages, searching for 'Aeronautics Research (PhD)' and using reference number AE0078. List Prof. Oliver Buxton as the research supervisor and Aero as the research group. The application deadline is 31 May 2026.

Funding details

Available

What's required

Candidates must have achieved or expect to achieve a First class honours MEng/MSci or higher degree (or international equivalent) in Aeronautical/Mechanical Engineering or similar STEM subjects. Willingness to learn new skills and techniques is required. Due to the competitive nature of the studentship, strong academic performance is expected. There may be a requirement to travel to The Netherlands for collaboration for periods of a few weeks.

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Professors

Professor at Imperial College London

University Name
Country flag

United Kingdom

Research Interests

Fluid Mechanics

Aerodynamics

Large Eddy Simulation

Viscous Flow

Turbulence

Professor at Imperial College London

University Name
Country flag

United Kingdom

Research Interests

Aerospace Engineering

Statistics

Machine Learning

Physics

Mechanical Engineering