PhD in Physics

Start Date: Between 1 August 2026 and 1 July 2027 This project aims to frame hypersonic aerodynamics as a grand inverse problem. By combining modern state-of-the-art AI (foundation models, physics-informed learning) with hard physical constraints (Navier–Stokes in spectral space) we will develop methods to super-augment experimental data via data assimilation and turn sparse wind-tunnel measurements from world-class facilities into rich, high-fidelity reconstructions of complex hypersonic flow fields. This new capability will uncover hidden flow drivers and closures for unknown physics, and ultimately allow us to design robust, manufacturable, and effective passive flow control concepts using smart materials and geometries for the next wave of hypersonic flight.