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sensors to deliver resilient, high-accuracy positioning. The project sits at the intersection of navigation, AI-enhanced signal and data analysis, and wireless communication systems, with applications in
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
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Francesco Fanicchia is a recognised expert in advanced surface engineering and the development of multifunctional protective coatings, specialising in thermal barriers and fire-resistant materials. As a
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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honours degree in materials science, physics, engineering, or a related discipline. The ideal candidate will be self-motivated, with an interest in both computational modelling and practical manufacturing
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attention on food safety, climate-resilient agriculture, and regulatory controls, accurate detection and risk assessment of such mycotoxins have become critical components of modern food science, toxicology
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doctoral training programme dedicated to academic research in space propulsion. R2T2 PhD programmes are already underway at nine UK universities, and the programme overall is centred on the Westcott facility
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in radiation–matter interactions, computational modelling, and materials science, with a strong publication record (h-index 36, i10-index 69). Dr Francesco Fanicchia, Research Area Lead: Material
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
complex engineering data and deliver insights that are robust, adaptable, and applicable across complex, high-value, safety-critical domains. This research will contribute to shaping the next generation of
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the computational inefficiencies of physics-based models and enabling faster, potentially more accurate predictions. However, AI models require substantial volumes of high-quality, labelled training data, which