Silvestre Pinho

[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 the opportunity to join a world-class research team in the Department of Aeronautics, working on the development of artificial intelligence and advanced numerical methods for structural aircraft digital twins. The project is part of the EPSRC Fellowship VERA and involves collaboration with leading organisations such as NASA, Airbus, and Rolls-Royce. The successful candidate will work in the PinhoLab, led by Professor Silvestre Pinho, which is renowned for its contributions to numerical and experimental methods in aeronautics and sustainability. The lab's work has been featured in TEDx talks, the UK Parliament, and major media outlets, and its alumni are highly sought after in aerospace, space agencies, technology companies, and academia. The main goal of the project is to develop efficient structural simulation techniques for novel aircraft configurations, supporting the aviation sector's transition to net-zero emissions by 2050. The research will expand the role of simulation in aircraft certification, accelerating innovation cycles. The studentship provides training in finite element analysis, scientific machine learning, uncertainty quantification, and professional programming standards, as well as a deep understanding of aircraft design and certification processes. There are opportunities to collaborate with global leaders, present research at international conferences, and gain experience in co-supervising MSc theses and assisting with undergraduate labs. The position is fully funded for 3.5 years, covering tuition fees and providing an annual tax-free stipend of £22,780 for Home, EU, and International students. Applicants must hold or expect to hold a First class honours MEng/MSci or higher degree in Aeronautics, Aerospace, or Mechanical Engineering, with a strong affinity for programming. Experience with numerical methods, finite element method, statistics, and machine learning is desirable. The application process involves submitting a CV, transcripts, and a motivation statement, followed by a supervisor review and further instructions for formal application. Imperial College London is committed to equality, diversity, and inclusion.