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uncertainty and dynamic conditions. In complex electronic systems, ensuring reliability and minimizing downtime are critical challenges. AI-driven fault diagnosis and self-healing electronics offer innovative
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modelling tools to understand and tailor the physical and chemical interactions at the interfaces within metascintillators. Cranfield University’s Centre for Materials is internationally recognised
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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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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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow
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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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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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modelling to provide a robust framework for integrating nature-based solutions into SO management. This can alleviate the pressure on treatment infrastructure and reduce dependence on grey infrastructure
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of complex, dynamic flows relevant to closely coupled engine aircraft configurations. You’ll join a pioneering multidisciplinary team that values equity, diversity, and inclusion, gaining unique expertise in
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reliability and maintenance strategies. Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter