Physics-informed multi-agent AI for cooperative formation-flying orbital transfers
The Faculty of Science Doctoral Training Centre (DTC) in Artificial Intelligence (AI) is a new initiative by the University of Nottingham to train future researchers and leaders to address the most pressing challenges of the 21st Century, through foundational and applied AI research on a cohort basis. The DTC training and supervision will be delivered by a team of outstanding scholars from different disciplines cutting across arts, engineering, medicine and health sciences, science, and social sciences.
Project overview
Start date: 1 October 2026 for 3.5 years (42 months)
Satellites operating in coordinated groups require autonomous, fuel-efficient, and safe reconfiguration strategies to realise next-generation Earth observation, navigation, and in-orbit services missions. This project will address these challenges by developing novel AI-driven methods for cooperative orbital transfers across satellite formations. The research will combine multi-agent reinforcement learning (MARL) with physics-informed modelling of relative orbital dynamics to enable distributed decision-making among spacecraft under realistic perturbations (e.g. J₂, drag, solar radiation pressure) and communication constraints. You will design learning frameworks that explicitly incorporate collision avoidance, fuel efficiency, and real-time adaptability, moving beyond centralised optimisation to scalable, agent-level autonomy.
The project will be supported by an interdisciplinary supervisory team spanning engineering and computer science, providing multidisciplinary training in AI, control theory, optimisation, and astrodynamics. You will benchmark AI methods against classical transfer and formation control techniques and validate solutions using high-fidelity simulations and hardware-in-the-loop testbeds. This work is ideal for applicants with interests in AI, autonomous systems, and space sciences.
Supervisors
- Dr Nishanth Pushparaj - Faculty of Engineering
- Dr Chantal Cappelletti - Faculty of Engineering
- Dr Nikhil Deshpande - School of Computer Science
Candidate requirements
Applicants should hold (or expect to obtain) a first-class undergraduate degree (or equivalent), or a distinction at postgraduate level in Aerospace Engineering, Mechanical Engineering, Electrical Engineering, Computer Science, Physics, or a closely related discipline.
Read our application guide for full guidance on residency, qualifications and English language requirements.
Essential skills
- Strong mathematical foundations e.g. linear algebra, optimisation, and dynamical systems
- Proficiency in Python
- Experience or a strong interest in both machine learning and dynamical/control systems
Desirable experience
- Reinforcement learning
- Multi-agent systems
- Orbital mechanics
- Numerical simulation
Funding and eligibility
This studentship is open to UK/home applicants only.
Annual tax-free stipend based on the UKRI rate (£21,805 for 2026/27), home tuition fee, and a £3,000 p.a. Research Training Support Grant.
How to apply
Application deadline: Sunday 19 April 2026. You must have completed and submitted your application to the NottinghamHub system by this date.
Read our application guide for full guidance on how to apply. The application process has two steps.
Email Dr Nishanth Pushparaj (Faculty of Engineering) for further details and to arrange an interview.