Machine Learning for Air-breathing Hypersonic Propulsion Kingston University in United Kingdom

This PhD project at Kingston University offers an exciting opportunity to apply advanced Machine Learning (ML) techniques to the aerodynamic challenges of air-breathing hypersonic propulsion systems, with a particular focus on SCRAMJET engine design and operation. The research aims to address critical issues in the design of air-intakes and their impact on local flow fields, which are essential for the performance and efficiency of hypersonic flight vehicles.

Through the use of ML, the project will systematically evaluate previous design work and studies on SCRAMJET engines, seeking innovative solutions for intake design—a key feature in any high-speed flight vehicle. A deep understanding of local flow characteristics, such as shock wave boundary layer interactions, will be developed to inform successful intake designs. The ML-driven approach will facilitate the evaluation and de-risking of proposed designs, which will then be modelled using Computational Fluid Dynamics (CFD) to compare their performance against current state-of-the-art solutions.

Students undertaking this project will gain expertise in high-speed propulsion design, numerical and computational modelling, and the application of machine learning to complex engineering problems. The research is supported by the Faculty of Engineering, Computing and the Environment, and is part of the Graduate School studentships competition for October 2026 entry, offering funding opportunities for eligible candidates.

Applicants should have a strong background in Aerospace Engineering, Mechanical Engineering, Computer Science, or a closely related discipline, with a keen interest in ML, aerodynamics, and computational methods. International candidates may need to demonstrate English language proficiency. The application deadline is March 4, 2026, and further details on funding and application procedures can be found on the Kingston University PhD Studentships and Faculty research webpages.

This project is ideal for students passionate about cutting-edge aerospace research, computational modelling, and the integration of ML in engineering design. Successful candidates will join a vibrant research community and work under the supervision of Dr M Claus, contributing to advancements in hypersonic propulsion technology.