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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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: 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
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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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at international conferences and build a professional network across academia and industry. Development of expertise in cutting-edge experimental techniques, computational modelling, and interdisciplinary
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accuracy is still limited. In contrast, computational fluid dynamics (CFD) models can capture the arc physics and molten pool dynamics, including arc energy transfer and liquid metal convection within
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predictive accuracy and prohibitively long computational times, making them unsuitable for real-time process control. Artificial intelligence (AI) models present a promising alternative by addressing
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Resilience (WIRe) , a prestigious collaboration between Cranfield University, the University of Sheffield, and Newcastle University. The WIRe programme offers bespoke training that hones both technical and
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intensive training in energy modelling, AI-accelerated optimisation, and lifecycle-aware computing. Whether working on smart mobility, sensor nodes, or autonomous platforms, you’ll be contributing to a new
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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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refine simulation tools and machine learning solutions to advance stroke treatment. This involves improving existing computational models that simulate cerebral blood flow, oxygen distribution, and brain