Machine-Learning for High-Speed Aerial Vehicle Control
The Department of Aerospace Engineering at the University of Bristol invites applications for a fully funded PhD Studentship titled “Machine-Learning for High-Speed Aerial Vehicle Control.” This research project investigates new machine learning techniques, including physics-informed neural networks for dynamical systems, to control high-speed and highly manoeuvrable aerial vehicles.
The primary aim of the project is to bridge the gap between classical control theory and modern machine learning methods across both high-level path planning and low-level flight control. A principal research objective will be developing a physics-informed, meta-learned adaptive guidance approach that can instantly adapt the guidance and control system to new tracking targets, atmospheric conditions, or vehicle states using only a few real-time measurements.
Key Highlights & Opportunities: • Co-funded by a leading industrial partner in the defence sector. • Opportunity to undertake a industrial work placement at the partner organization’s site. • Expected project start date is no later than March 2027.]
[Eligibility Criteria: • The successful candidate must qualify for UK Home student fee status. • Expected to successfully obtain UK security clearance. • Candidates must confirm their fee status when contacting supervisors. • Academic background: Holds (or expects to achieve) a First or Upper Second-class (2:1 or above) undergraduate degree in a STEM discipline (Engineering, Mathematics, Physics, Computer Science, or related field).]
[Required expertise/skills: • Proficiency in MATLAB and Simulink. • Foundation or knowledge in flight control and dynamical systems. • Background or strong interest in machine learning, particularly physics-informed neural networks. • Relevant academic or industrial experience in aerospace engineering or autonomous control systems is desirable.]
[Salary details: Fully funded PhD studentship covering UK Home tuition fees and providing a standard annual tax-free EPSRC stipend.]
[Application Deadline: November 4, 2026]