[Full tuition fees and annual tax-free stipend of £22,780 for Home, EU and International students for 3.5 years.] This PhD studentship at Imperial College London focuses on the interaction between offshore wind farms and cloud formation, aiming to maximise wind farm performance using scientific machine learning. The project investigates how the operation of large offshore wind farms modifies the marine boundary layer (MBL), particularly in conditions conducive to marine stratocumulus (MS) cloud formation. The central research questions address whether turbine-driven changes in the MBL promote or hinder MS formation, and how these changes affect the local mesoclimate and wind farm power generation. The research employs high-fidelity large eddy simulation (LES) codes and scientific machine learning tools, including real-time optimisers, to simulate wind farms under various atmospheric inflows. Some code development will be required to implement actuator disc/line wind-turbine models. The project aims to develop SML-based strategies for discovering self-similarity laws, quantised local reduced order models, and real data assimilation. The successful candidate will join the research groups of Prof. Oliver Buxton (expert in turbulence, wind-energy flows, and turbulent cloud microphysics) and Prof. Luca Magri (expert in scientific machine learning for aeronautical applications). Both groups host ERC projects, offering opportunities for collaboration and interdisciplinary research. The studentship is fully funded for 3.5 years, covering tuition fees and providing a generous annual stipend of £22,780 for Home, EU, and International students. Applicants must hold or expect to hold a First class honours MEng/MSci or equivalent in Aeronautical/Mechanical Engineering or a related STEM field, and be willing to learn new skills. There may be opportunities for short-term collaboration with a group in The Netherlands. The application process involves submitting a CV, transcripts, and a motivation statement for supervisor review, followed by a formal application for long-listed candidates. Imperial College London is committed to diversity and inclusion, as reflected in its awards and partnerships.

[Full tuition fees and annual tax-free stipend of £22,780 for Home, EU and International students.] This PhD studentship at Imperial College London offers a unique opportunity to investigate the complex interactions between offshore wind farms and cloud formation, with a focus on maximising wind farm performance using scientific machine learning. The project addresses how the operation of large offshore wind farms modifies the Marine Boundary Layer (MBL), influencing the formation of marine stratocumulus clouds and, in turn, affecting local mesoclimate and wind farm power generation. The research will use high-fidelity large eddy simulation (LES) codes and scientific machine learning tools, including real-time optimisers and actuator disc/line wind-turbine models, to simulate wind farms under various atmospheric conditions. The student will develop strategies for discovering self-similarity laws, quantised local reduced order models, and real data assimilation, gaining deep insight into the flow physics of cloud formation and wind energy. The successful candidate will be jointly embedded in the research groups of Prof. Oliver Buxton (expert in turbulence, wind-energy flows, and cloud microphysics) and Prof. Luca Magri (expert in scientific machine learning for aeronautical applications). Both groups host ERC projects, providing a vibrant and collaborative research environment. The studentship is fully funded for 3.5 years, covering tuition fees and providing a generous annual tax-free stipend of £22,780 for Home, EU, and International students. There is potential for international collaboration, including short research visits to The Netherlands. Eligibility requires a First class honours MEng/MSci or higher degree (or international equivalent) in Aeronautical/Mechanical Engineering or a closely related STEM field. Applicants should demonstrate strong academic performance and a willingness to learn new skills and techniques. The application process involves submitting a CV, transcripts, and a motivation statement for supervisor review, followed by a formal application for long-listed candidates. The deadline for applications is 8 January 2026. Imperial College London is committed to diversity and inclusion, holding Athena SWAN Silver, Stonewall Diversity Champion, and Disability Confident Employer status, and partnering with GIRES to promote respect for trans people.