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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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and brain tissue mechanics to improve stroke treatment. Stroke is a leading cause of death and disability worldwide, making advancements in its diagnosis and treatment highly relevant. Computational
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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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Rolls-Royce, this project will use both experimental and computational aspects to explore the aerodynamic design space for coupled intake/fan configurations that are required to deliver more efficient
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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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equivalent in a relevant discipline such as aerospace engineering, mechanical engineering, electrical engineering, computer science/engineering, applied physics/mathematics, or related fields. Prior experience
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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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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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simulating fluid networks and dynamic phenomena for assessing different solutions is a necessity The overall aim of this project is to improve the confidence in fuel system design process for ultra-efficient
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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