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(CHF), tailored to complex geometries typical of fusion reactor cooling systems. Compile a comprehensive dataset of boiling parameters to support machine learning-based analysis of two-phase flow
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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compressible gas dynamics, heat transfer, free-surface/melt behaviour, and mass transfer driven by phase change, within a GPU-accelerated solver to reduce simulation turnaround times. You will develop and
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these robots utilise electronic, chemical, pressure, magnetic, or thermal mechanisms, with the current generation having significant drawbacks, including low energy efficiency, high operating voltage
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rigorous approach to problem-solving. Applicants should have, or be expected to gain, a high 2:1 (preferably 1st class) honours degree in Mechanical or Aerospace Engineering, or a related discipline with
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, engineering design, and AI-enabled design and optimisation that will transform current product development practices. Together we will develop the next-generation product-production co-design platform
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the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading
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the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading
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; stem cells; and surgery. A number of pivotal discoveries have been made in Nottingham with current research continuing to provide exciting data, improving our understanding of breast cancer and
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Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading academics from 5 universities, and 11