PhD Studentship in Aeronautics: AI-Driven Inverse Modelling & Design Optimisation for Next-Gen Hypersonic Flight Imperial College London in United Kingdom

[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 offers an exciting opportunity to work at the intersection of aeronautics, AI, and advanced physical modelling for next-generation hypersonic flight. The project aims to reframe hypersonic aerodynamics as a grand inverse problem, leveraging state-of-the-art AI techniques such as foundation models and physics-informed learning, combined with hard physical constraints like the Navier–Stokes equations in spectral space. The successful candidate will develop methods to super-augment experimental data through data assimilation, transforming sparse wind-tunnel measurements into rich, high-fidelity reconstructions of complex hypersonic flow fields.

This capability will help uncover hidden flow drivers and closures for unknown physics, ultimately enabling the design of robust, manufacturable, and effective passive flow control concepts using smart materials and geometries. The student will build an AI and physics assimilation pipeline, compile and critically evaluate experimental databases, and develop a coherent testbed for assimilation approaches. The project involves inferring unknown quantities of high-speed wall-bounded flows from data under spectral Navier–Stokes constraints and using these models to co-design passive controls. Validation will be performed on both synthetic and real experiments, with opportunities to publish open benchmarks and research papers.

The training environment is highly collaborative and impact-driven, with support for paper writing, presentations, and conference travel. The student will gain deep skills in hypersonic flows, AI for PDEs, data assimilation, and reproducible HPC workflows using Python, C++, PyTorch, and JAX. The position is funded for 3.5 years, covering full 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 Engineering, Applied Mathematics, Physics, or a closely related field, with strong backgrounds in aerodynamics, CFD, applied maths, or scientific computing, and proficiency in Python/C++. Exposure to machine learning or automatic differentiation is advantageous. The application process involves submitting a CV, transcripts, and a motivation statement, followed by supervisor review and further instructions for long-listed candidates.

The lab values equality, diversity, and inclusion, and collaborates with leading experimental facilities at Imperial and international partners.