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complex in scenarios like parallel-pass deposition, thin-wall deposition, and off-centre or out-of-position deposition. Additionally, FEA models are focused on thermal conduction in solid medium and often
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digital health systems. The project’s emphasis on explainability, traceability, and system integrity positions graduates as key contributors to the future of responsible AI deployment.
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the global surge in demand for energy-efficient AI solutions, this research positions students at the forefront of technological innovation, equipping them with the expertise to lead in the development of next
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encourage early submission, as the position may be filled before the stated deadline.
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
professional and transferable skill development, preparing graduates for careers in aerospace, engineering, and digital innovation. Throughout the PhD, the student will develop a broad set of skills, from
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the International Systems Realisation Partnership, as well as potential exposure to travel, conferences, and external training opportunities. The student working on this project will gain a diverse set of
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complimentary computational studies to predict the intake aerodynamic characteristics and aid in the experiment design. This position is part of the CDT in Net Zero Aviation, which offers a modular, cohort-based
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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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generated by such collisions can indeed remain in space for a long time and pose a secondary risk to the constellation itself. Even small debris particles with diameters of a few millimetres can cause
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, aerospace materials, and sustainable infrastructure design. Your research will directly support the UK's ambition to achieve Net Zero aviation by 2050, positioning you as a skilled innovator and inclusive