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Aviation by 2050. This exciting doctoral project, in collaboration with Rolls-Royce, will develop innovative computer vision methods which when combined with optical flow velocimetry will enable imaging
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with: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in
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at the edge. The project explores advanced topics such as TinyML, neuromorphic design, reconfigurable logic, and autonomous fault recovery, with applications ranging from aerospace, energy, and robotics
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates, hands on skills in implementation of fusion/learning based techniques in
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reusable launchers, autonomous robotics, and advanced materials could redefine how we design space structures. The ability to remotely assemble orbital systems from multiple launcher payloads would allow
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, computer vision or flow measurement background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience of computer coding in some form or any discipline is also