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Development courses and unique in the academic sector, industry-scale experimental facilities. The interview process will involve applicants demonstrating alignment of technical competency and motivation
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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knowledge co-evolution and addressing complex challenges in a super-intelligent society. This project is situated within the rapidly evolving field of Cyber-Physical-Social Systems (CPSS), which is of
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with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience
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elements like Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) to secure hardware components. Embedded Trust Protocols: Design protocols that establish and maintain trust within
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models and physics-based models. More recently, hybrid prognostics approaches have been presented, attempting to leverage the advantages of combining the prognostics models in the aforementioned different
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Verification Tools: Develop AI algorithms that automate the verification process, ensuring systems meet required safety and performance standards. Health Monitoring Algorithms: Implement AI-based monitoring
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, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions. This PhD at Cranfield University explores the development of resilient, AI-enabled
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A funded PhD studentship is available within the Autonomous and Cyber Physical Systems Centre at Cranfield University, Bedfordshire, UK. As aerospace platforms go through their service life, gradual