We are advertising a Research Assistant post to contribute to the European Space Agency project “Robust Real-Time Constrained Optimal Control using Machine Learning” working with Dr Kevin Worrall and Dr Matteo Ceriotti, in collaboration with industrial partner Craft Prospect Ltd. and within the Space and Exploration Technology (SET) group.

Job Description

This project is an exciting opportunity to be involved in cutting-edge research aiming to demonstrate the effective use of Machine Learning (ML) for the robust optimal control of space systems. The objective of this project is to leverage recent advancements in ML and embedded optimization to develop a methodology for optimal spacecraft motion planning and control that is both computationally efficient to be deployed on space hardware and can handle known modelling uncertainties, navigation errors and stochastic disturbances by design. The capabilities of the novel motion planning technology will be demonstrated in simulation on challenging benchmark use cases relevant for current and upcoming space missions where the ML-enhanced trajectory planning and optimal control methods can bring the most added benefit. Examples of such mission scenarios include autonomous precision landing, guided atmospheric re-entry, spacecraft rendezvous, in-orbit servicing and proximity operations. The computational performance and reliability of the machine-learning-based motion planner will also be verified on flight representative computing hardware.

The outcomes will be supported by comprehensive documentation, describing the design, use and verification of the solutions and simulator.

The successful candidate will also be expected to contribute to the formulation and submission of research publications and research proposals as well as help manage and direct this complex and challenging project as opportunities allow.

Application

Full details and link to the application process at:

https://www.jobs.gla.ac.uk/job/research-assistant-13

Closing date for application: 12 March 2025

First published: 26 February 2025

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