Postdoctoral Research Associate or Research Assistant in Data-Driven Fuel Cell Modelling

This position offers two distinct roles—Postdoctoral Research Associate or Research Assistant—to advance data-driven modelling of fuel cell dynamics within an Airbus- and Aerospace Technology Institute (ATI)-funded project. The work focuses on creating interpretable, nonlinear dynamic models of fuel cell electrochemistry using the Sparse Identification of Nonlinear Dynamics (SINDY) algorithm. The successful candidate will collaborate with interdisciplinary teams across Imperial College London’s Chemistry and Mechanical Engineering groups to improve fuel cell performance, reliability, and control for next-generation hydrogen-powered aircraft. The role involves both computational and experimental approaches, bridging machine learning and electrochemical engineering.

• Develop nonlinear dynamic models of fuel cell electrochemistry using advanced data-driven techniques.
• Apply the Sparse Identification of Nonlinear Dynamics (SINDY) algorithm to numerical and experimental data.
• Identify interpretable reduced-order models for fuel cell control systems.
• Collaborate closely with researchers in Chemistry and Mechanical Engineering groups.
• Contribute to the development of computational and experimental methods for fuel cell performance optimization.
• Work within a multidisciplinary team to improve design, control, and reliability of fuel cells for hydrogen-powered aircraft.