PhD Studentship in Aeronautics: Developing a Digital Twin for Composite Pressure Tanks Imperial College London in United Kingdom

[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 contribute to the future of hydrogen-powered aviation by developing a digital twin for composite pressure tanks. Hydrogen fuel is a promising clean energy source for aviation, but its adoption is limited by significant storage and safety challenges. The project addresses these challenges by focusing on the design and monitoring of lightweight, robust composite tanks capable of safely storing hydrogen under extreme conditions.

The research will integrate advanced Structural Health Monitoring (SHM) and Digital Twinning (DT) technologies to provide real-time insights into the condition of hydrogen storage systems. Methodologies such as acoustic emission will be developed to detect early signs of damage, leaks, or material degradation, enhancing the safety and reliability of composite tanks. The project will also leverage machine learning, physical modeling, sensor data, and computational mechanics to create a virtual replica of the tank, enabling predictive maintenance and lifetime assessment.

Supervised by Professor Zahra Sharif Khodaei and Professor M.H. Ferri Aliabadi, both experts in aerospace structures and health monitoring, the successful candidate will gain expertise in computational mechanics, structural health monitoring, and machine learning. The research is highly mathematical and fundamental, with potential impacts across diverse engineering fields.

The studentship 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 equivalent in Aeronautics or Mechanical Engineering, with strong skills in structural mechanics and computational methods. Familiarity with machine learning and SHM is desirable, and candidates should be motivated for fundamental research.

Applications are open until 8 January 2026. The process involves submitting a CV, transcripts, and a motivation statement via the Supervisor Review Form. Long-listed candidates will be invited to formally apply. Imperial College London is committed to equality, diversity, and inclusion, and welcomes applicants from all backgrounds.