[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 work at the intersection of aeronautics, mechanical engineering, materials science, and computer science. The project aims to develop architected materials and metamaterials with targeted non-linear thermo-mechanical responses, suitable for both terrestrial and aerospace applications. The research will integrate machine learning-based inverse design and topology optimisation to create multiscale materials capable of handling variability and uncertainty from sources such as material properties, fabrication processes, and boundary conditions. The successful candidate will build computational frameworks using advanced ML techniques, including tandem neural networks, video diffusion models, reinforcement learning, and mixture density networks, to efficiently explore high-dimensional design spaces and enable robust property-to-design mapping. The project is hosted by the IDEA Lab and Structural Metamaterials Group, which provide a collaborative and inclusive research environment, access to professional development, and opportunities to enhance both technical and interpersonal skills. Funding covers full tuition fees and a generous 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, Mechanical Engineering, or Computer Science, and should have experience in machine learning, optimisation, Python, and finite element analysis. The studentship lasts 3.5 years, with a flexible start date between August 2026 and July 2027. 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 equality, diversity, and inclusion, supporting a welcoming environment for all researchers.