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Drones are changing in shape, design, algorithms, and operation control. Understanding their internal states is important for predicting threat to infrastructure and assets. Short term conflict alerting for primary surveillance system (e.g., radar systems) requires not only detection and classification, but also inference of mission intent.

The PhD will focus on working with academics and the sponsoring company Saab (UK) and Saab Sweden researchers in:

1. Developing robust intention prediction machine learning algorithms such as inverse reinforcement learning.
2. Using synthetic and real radar data to solve real problems.
3. Developing the evidence base to inform partners in radar and policing.
4. Working with partners on case studies 5. Writing high quality academic papers.

References of our past work are here:

1. "Uncovering Drone Intentions using Control Physics Informed Machine Learning," Nature Communications Engineering, vol.3, Feb 2024.
2. "Uncovering Reward Goals in Distributed Drone Swarms using Physics-Informed Multi-Agent Inverse Reinforcement Learning," AIEEE Transactions on Cybernetics, Nov 2024.
3. "Closed-Loop Output Error Approaches for Drone's Physics Informed Trajectory Inference," IEEE Transactions on Automatic Control, 2023.

You will be working world-leading scholar, Professor Weisi Guo, on the topic partnered with one of the industrial giants from the aerospace sector (Saab).

The work is envisioned to have great impact on design and development of intelligent radar data processing modules and inform both air surveillance and police stakeholders.

Fully funded PhD covering not only tuition, fees and bursary but opportunity to attend conferences and to link with industrial experts in the field.

The applicant is envisioned to further enhance and develop world class skills in AI and Machine Learning with application to hard and challenging drone problems providing a great skill set for employability after the degree in both industry but also academia as well.