PhD Studentship: Machine-learning for High-speed Aerial Vehicle Control

Overview

The University of Bristol is offering a fully funded PhD studentship on machine learning for the control of high-speed aerial vehicles. The project will investigate new machine learning techniques, including physics-informed neural networks for dynamical systems, to control highly manoeuvrable aerial vehicles. It emphasises bridging classical control and modern machine learning, and bridging high-level path planning with low-level flight control.

A principal aim is a physics-informed, meta-learned adaptive guidance approach that can immediately adapt the guidance and control system to new tracking targets, atmospheric conditions or vehicle states using only a few real-time measurements. The work is co-funded by a leading industrial partner in the defence sector, and there is an opportunity for a work placement at the partner's site.

Requirements

Desirable skills and experience include a 2.1 or above undergraduate degree in a STEM subject, MATLAB and Simulink, flight control, dynamical systems, machine learning, and relevant academic or industrial experience.

Who can apply

The successful candidate must qualify for UK home student status and is expected to obtain UK security clearance. Candidates are asked to confirm their fee status when contacting the supervisors.

What you get

The studentship is fully funded with the standard EPSRC stipend.

How to apply

Applicants make an online application to the university, selecting the PhD in Aerospace Engineering on the programme choice page, and contact one of the supervisors.

Key dates

The closing date is 4 November 2026, and the expected start date is no later than March 2027.