Luca Magri

[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 coverage of 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 developing real-time machine learning and optimisation systems to address extreme weather challenges in aeronautics. As climate change intensifies, atmospheric events such as turbulence, storms, and shifting jet streams increasingly threaten flight safety and efficiency, leading to fuel wastage, longer travel times, and greater environmental impact. The project aims to enable aircraft to reroute safely and efficiently in real time as weather conditions evolve, moving beyond conventional pre-flight planning. The research is structured into three main stages: First, the student will create a scalable, automated pipeline to collect and harmonise heterogeneous datasets from sources like OpenFlights, OpenSky Network, Aviation Weather Center, and ADS-B providers, integrating real-time flight trajectories, route networks, and high-resolution weather data. Second, machine learning models will be developed to analyse these integrated data streams, identifying early signs of weather-induced disruptions and forecasting hazards such as turbulence and storm activity using graph-based clustering, fuzzy machine learning, and reduced-order models. Third, the student will design adaptive algorithms within a bias-aware ensemble Kalman filter framework to dynamically propose alternative flight paths, maximising safety and fuel efficiency while minimising congestion and emissions. The final deliverable will be a user-friendly decision-support tool for real-time rerouting recommendations, directly supporting pilots and flight control operations. The project is supervised by Professor Luca Magri and offers full funding, including tuition and a generous stipend for Home, EU, and International students. The studentship lasts 3.5 years and is open to candidates with a strong computational background in engineering, physics, mathematics, or computer science, with a First class honours degree or equivalent. The research will contribute to sustainable aviation practices and climate-aware air traffic management, with models evolving through real-world updates. Imperial College London is committed to equality, diversity, and inclusion, and encourages applications from all backgrounds.

[Full coverage of tuition fees and an annual tax-free stipend of £22,780 for Home, EU and International students.] This PhD studentship at Imperial College London offers an exciting opportunity to advance the prediction of extreme events in turbulent reacting flows using scientific machine learning. The project is motivated by the increasing prevalence of extreme fluid events due to climate change and the global push for decarbonization. These rare events, such as flashback with hydrogen or blow-off with ammonia, pose significant risks in future sustainable propulsion systems. Current forecasting methods are limited by the chaotic nature of turbulent flows and complex multiscale nonlinear interactions. As a student, you will develop machine learning-based reduced-order models capable of accurately forecasting extreme events across a variety of complex flows. The research will involve high-fidelity simulations, hybrid physics-based and machine learning modelling techniques, and embedding these models within data assimilation frameworks for self-correction. You will gain expertise in computational modelling, scientific machine learning, and fluid dynamics, and have access to Imperial College's state-of-the-art HPC facilities. The project is part of the ERC-funded CONTEXT initiative, working at the intersection of scientific machine learning and fluid dynamics. You will be supervised by Dr. Anh Khoa Doan and have opportunities to collaborate with experts such as Prof. Luca Magri and Dr. Andrea Novoa. The studentship lasts 3.5 years and includes full coverage of tuition fees and an annual tax-free stipend of £22,780 for Home, EU, and International students. Eligibility requires a First class honours MEng/MSci or higher degree or equivalent in a computational background (engineering, physics, mathematics, or computer science). Strong skills in computational modelling, machine learning, and fluid dynamics are highly desirable. Imperial College London is committed to equality, diversity, and inclusion, holding awards and partnerships that promote a supportive environment for all researchers. To apply, submit your application via the Imperial College Apply webpages, selecting 'Aeronautics Research (PhD)' and using reference number AE0086. Indicate Dr Anh Khoa Doan as your research supervisor and Aero as your research group. For project-specific questions, contact Dr. Anh Khoa Doan at [email protected]; for application process queries, contact Lisa Kelly at [email protected]. The application deadline is 1st April 2026, and the position is available to start as soon as possible. For further information on fees and funding, visit the provided links. Imperial College London offers professional development workshops and support through its Early Career Researcher Institute, ensuring a comprehensive academic and professional experience.

[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.