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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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to support condition-based predictive maintenance for gas turbine engines. Cranfield has developed unique physics-based technologies on gas turbine performance simulations, diagnostics, prognostics and lifing
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and nanomaterials at the Composites and Advanced Materials Centre (Dr Sameer Rahatekar, Prof Krzysztof Koziol) and Hyper-velocity impact testing facilities at Centre for Defence Engineering and 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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aerodynamics will continue to be an important aspect including the improvement of fundamental understanding of complex flow physics as well as advancing aerodynamic methods for industrial design. The overall
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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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of these complex designs will need advanced measurements from early development to flight testing. Optical sensing can provide high-resolution data to understand the underlying physics, potentially reducing
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-efficient research that prevents fatigue failures has pushed towards integrated computational materials engineering approaches that improve competitiveness. These approaches rely on physics-based models
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with a background in mechanical, aeronautical, automotive, civil / industrial and/or software engineering (or similar) and/or mathematics and/or physics. The ideal candidate will have a solid background
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industry, academia, government, and policy. The interview process is composed of two interviews. Following a first introductory interview (20min), a second online (or face to face if preferred) interview