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and controlling defects and lay the foundation for a thermal physics-based approach to process qualification. Additive manufacturing (AM) is a rapidly evolving technology that continues to drive
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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
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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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of representative failure models for gear failures causes difficulties in their useful lifetime prediction. Critical operational parameters such as loading, speed and lubrication affect the physics of gear meshing
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of first or second class UK honours degree or equivalent in a related discipline, science (chemistry/physics) or engineering. The ideal candidate should have some understanding in the area of materials
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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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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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community. We welcome students and staff from all backgrounds from over 100 countries and support our staff and students to realise their full potential, from academic achievement to mental and physical
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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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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