PhD Position: Autonomous Collision Avoidance for Low-Thrust Spacecraft

This Marie Curie COFUND PhD position at Université de Toulouse and Universidad de Sevilla focuses on designing autonomous collision avoidance guidance for low-thrust spacecraft. The project combines Pontryagin’s Minimum Principle with machine learning to create fuel-efficient, computationally efficient, and robust algorithms for real-time orbital maneuvering. The PhD candidate will work across Toulouse (primary) and Sevilla (with mandatory 1-year stay), addressing critical challenges in space debris mitigation and satellite constellation operations. The research aims to reduce reliance on human intervention in collision avoidance maneuvers, ensuring sustainable space operations amid growing orbital congestion. Applicants must meet MSCA mobility rules and demonstrate strong expertise in control engineering or aerospace systems.

• Develop hybrid algorithms combining Pontryagin’s Minimum Principle with neural networks for real-time collision avoidance in low-thrust spacecraft.
• Design computationally efficient optimal control strategies to minimize fuel consumption while respecting propulsion and battery constraints.
• Implement and validate Pontryagin Neural Networks (PoNNs) to solve Two-Point Boundary Value Problems (TPBVPs) for collision avoidance maneuvers.
• Conduct stability and robustness analyses for Model Predictive Control (MPC) loops under dynamic orbital conditions.
• Perform rigorous validation of the inference model, including benchmarking against state-of-the-art tools.
• Collaborate with Université de Sevilla for mandatory research stays (minimum 1 year total).
• Publish findings in peer-reviewed journals and present results at international conferences.