[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.

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