Spencer Sherwin

[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 is part of a multi-centre research programme focused on parallel mesh generation for high-fidelity industrial simulations in aeronautics. The project addresses engineering challenges in areas such as Formula 1, turbomachinery, and nuclear fusion, where complex geometries and dynamic physics require adaptive mesh techniques. The successful candidate will develop novel algorithms for efficient load rebalancing and solver data structure updates in parallel simulations, targeting large-scale heterogeneous computing environments that utilize both CPUs and GPUs. As a member of the Nektar++ group, you will contribute to the development of advanced C++ software frameworks and gain hands-on experience with high-order finite element methods, mesh adaptation, parallel programming, and software engineering best practices. The studentship offers access to state-of-the-art computational resources, including Imperial College's HPC clusters and UK National Supercomputer facilities. Collaboration opportunities are available with researchers at Swansea University, Kings College London, Queens University Belfast, and the University of Edinburgh. The position 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 should hold or expect to obtain a First class honours MEng/MSci or equivalent degree in Engineering, Mathematics, Computer Science, Physics, or a related discipline, and should possess strong C++ programming skills, enthusiasm for collaboration, and good communication abilities. The application process involves submitting a CV, transcripts, and a motivation statement for supervisor review, with further instructions provided to long-listed candidates. Imperial College London is committed to equality, diversity, and inclusion, and encourages applications from all backgrounds.