PhD Studentship in Aeronautics: Architected materials for non-linear thermo-mechanical applications (AE0079)

Start Date: Between 1 August 2026 and 1 July 2027

Introduction: This project fuses machine learning (ML) based inverse design approaches and topology optimisation (TO) to realise multiscale architected materials or metamaterials (MTM) that can undergo targeted non-linear response. You will develop a computational framework that can reveal novel Multiphysics (thermo-mechanical) MTM solutions for demanding terrestrial and non-terrestrial (Aerospace) applications accounting for sources of variability and uncertainty, for example, those arising from material, fabrication process, and boundary and loading conditions. By generating datasets from finite element simulations, ML models can learn the mapping between unit cell design parameters and homogenised properties. State-of-the-art approaches – such as tandem neural networks, video diffusion models, and reinforcement learning – will be explored to efficiently navigate these high-dimensional, nonlinear design spaces.

To achieve robust property-to-design mapping, mixture density networks or MDN-based inverse generators will be employed to capture the multimodal distribution of the design space, enabling flexible inverse design sampling based on probability, property, geometric or printability criteria. Doing so, overcomes a key limitation of deterministic ML-based methods, such as tandem-NNs, which can only produce a single solution per target. Unlike other deterministic data-driven methods, probability measures derived in MDNs offer insights about inverse designs’ reliability and diversity, as well as the feasibility of the target properties.

We, the IDEA Lab and Structural Metamaterials Group, are proud to foster an inclusive, collaborative and thriving environment for our researchers. You will benefit from our expertise, background know-how (e.g. ML-based inverse design), access to professional development programmes, and opportunities to enhance both technical and interpersonal skills—within a truly world-leading research institution.

Supervisors: Dr Ajit Panesar and Prof. Rob Hewson

Duration: 3.5 years.

Funding: Full coverage of tuition fees and an annual tax-free stipend of £22,780 for Home, EU and International students.

Eligibility: Due to the competitive nature of these studentships, candidates will be expected to achieve/have achieved a First class honours MEng/MSci or higher degree (or international equivalent) in: Aeronautics, Mechanical Engineering or Computer Science.

We would also like to see experience in: Machine Learning, Optimisation, Python, finite elements

How to apply:

• Stage 1: Submit your 2-page curriculum vitae (CV), transcripts and a 300-word statement explaining your motivation for applying to this PhD Studentship to: Supervisor Review Form. Our supervisors will perform a comprehensive review to long-list candidates.

Deadline: 8 January 2026

• Stage 2: Supervisors will email further instructions and an application link to long-listed candidates, inviting them to make a formal application to the PhD Studentship.

Contact: For questions about the project: Dr Ajit Panesar

For queries regarding the application process, email Lisa Kelly, PhD Administrator.

Equality, Diversity and Inclusion: Imperial is committed to equality and valuing diversity. We are an Athena SWAN Silver Award winner, a Stonewall Diversity Champion, a Disability Confident Employer and are working in partnership with GIRES to promote respect for trans people.

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