About this role
The project:
This PhD project will investigate new machine learning techniques, including physics-informed neural networks for dynamical systems, for controlling high-speed and highly-manoeuvrable aerial vehicles. Emphasis will be placed on bridging the gap between classical control and modern machine learning methods, as well as high-level path planning and low-level flight control. like A principal research aim will be to develop a physics-informed, meta-learned adaptive guidance approach, which can instantly adapt the guidance and control system to new tracking targets, atmospheric conditions, or vehicle states using only a few real-time measurements.
The work will be co-funded by a leading industrial partner in the defence sector. There is also an opportunity to conduct a work placement at the industrial partner's site.
Desirable skills and experiences:
- 2.1 or above undergraduate degree in STEM
- MATLAB, Simulink
- Flight control
- Dynamical systems
- Machine learning
- Relevant academic or industrial experience
How to apply:
Please make an online application for this project at http://www.bris.ac.uk/pg-howtoapply. Please select ‘PhD in Aerospace Engineering’ on the Programme Choice page. You will be prompted to enter details of the studentship in the Funding and Research Details sections of the form.
Candidate requirements: The successful candidate must qualify for UK home student status and is expected to successfully obtain a UK security clearance. Candidates are requested to confirm their fee status when contacting one of the supervisors. The expected start date is no later than March 2027.
Funding: fully funded
Contacts:
Dr Duc Nguyen: [email protected]
Dr Bahadir Kocer: [email protected]
Professor Mark Lowenberg: [email protected]