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requirements A minimum of a 2:1 first degree in a relevant discipline/subject area (e.g. aerospace, automotive, mechanical, electrical, chemical, computing, and manufacturing) with a minimum 60% mark in
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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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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
mechanics, and artificial intelligence (AI)—specifically in the domains of non-destructive evaluation (NDE), computer vision, and machine learning. It addresses a critical challenge in the structural health
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mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all, the traditional
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mechanical, control or aerospace engineering, physics, mathematics, or other relevant engineering/science degree. The ideal candidate would have experience with computational modelling and control of dynamical
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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critical to ensuring the longevity and safety of fusion reactors. This PhD project focuses on developing an integrated framework that combines cutting-edge computational models, including Monte Carlo
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paramount. Secure and trustworthy AI-electronics focus on embedding security features directly into hardware, such as hardware security primitives and tamper detection mechanisms. This field addresses
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results from the Advanced Propulsion Centre (APC)-funded CERABEV project. The candidate will develop expertise in: Understanding of battery thermal runaway mechanisms and standards. Design and deposition
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield