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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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                additive manufacturing. This project will be closely aligned with the ATI research program (I-Break: Wire-based DED Technology Maturation and Landing Gear Application) and other industrial research projects 
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                metascintillator prototypes towards clinical application. The research will combine experimental surface engineering, advanced materials characterisation (SEM, XRD, spectroscopy), and performance testing alongside 
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                research program (I-Break: Wire-based DED Technology Maturation and Landing Gear Application) and other industrial research projects within WAMC. The student will become part of a diverse and dynamic 
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                . The project focuses on power-aware computing, thermal optimization, and sustainable electronic design, targeting critical applications in aerospace, healthcare, and industrial automation. Hosted by the renowned 
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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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                project would suit students with a background in electronics, embedded programming, signal processing, vibration measurement and analysis, maintenance engineering, and electro-mechanical engineering 
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                electronics, embedded programming, signal processing, vibration measurement and analysis, maintenance engineering, and electro-mechanical engineering. Funding This is a self-funded PhD. Find out more about fees 
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                with engineering, physics, mathematics, acoustics, fluids, electronics or instrumentation background. Prior experience in computational modelling is beneficial, but not mandatory. Similarly, experience 
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                evaluation. Prognostics is an essential part of condition-based maintenance (CBM), described as predicting the remaining useful life (RUL) of a system. It is also a key technology for an integrated vehicle