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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
between advanced sensing and analysis, enabling fast, reliable, and quantitative damage assessment of impacted composites. This project lies at the intersection of composite materials engineering, impact
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Honours degree and a Masters, or a Distinction at Masters level with any undergraduate degree (or the international equivalents) in Computer Science, Engineering, or Physics, we would like to hear from you
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to achieve, at least a 1st class honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. To apply, please contact the main supervisor, Dr Thomas
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good Master’s degree in Mathematics, Statistics, Computer Science, Engineering, Physics, with good programming, analytical problem-solving, writing and presentation skills. Please direct project specific
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to interact with GE Vernova’s engineering staff and will have the opportunity to work, for part of the time, in GE Vernova’s main HVDC design office, based in Stafford, UK. Here the candidate will have the
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matrix functions. These computational problems are central to many scientific and engineering applications, including quantum mechanics, materials science, and weather/climate modelling. Numerical methods
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A fully funded 3-year PhD studentship is available from 1st October 2025 in the School of Computer Science and Engineering at Bangor University. The project investigates viral infection in real-time
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molecular genetic engineering. Scientific Background In all species that lay eggs externally - in nests, ponds, mudpiles, food or other substrates - eggs and young may face unpredictable exposure to harmful
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the technology, hence there is a drive from the industry to address this. To answer this question requires an interdisciplinarity approach, combining Industry input alongside physical sciences and physiological
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PhD: Digital Optimisation of Rail Grinding EPSRC Centre for Doctoral Training in Machining, Assembly, and Digital Engineering for Manufacturing PhD Research Project Directly Funded UK Students Dr