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Field
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difficult for industry to predict and control product performance. This PhD project will tackle that challenge by applying advanced polymer characterisation techniques to better understand and quantify
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the risk of missed defects. Using the power of Artificial Intelligence (AI), this research aims to: Automate defect detection in complex 3D structural data Enhance diagnostic accuracy and processing speed
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which allows us to control and manipulate the rotational orientation projection states of hydrogen molecules in gas-surface collisions, which can classically be considered to correspond to whether
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randomised controlled trial (RCT) using a waiting list design. Fifty school pupils aged approximately 11 to 18 with non-anorexic eating disorders will be recruited from schools in the Midlands. Participants
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, complexity, and verification needs. By mapping each component to the most appropriate FM tool based on cost-efficiency and expected reliability gains, we aim to construct validation portfolios: automated
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Failure Analysis of Composite Sleeves for Surface Permanent Magnet Electrical Machines This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites
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This exciting opportunity is based within the Power Electronics, Machines and Control (PEMC) and Composites Research Groups at the Faculty of Engineering, which conduct cutting-edge research
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the understanding of offshore turbulence in spatially varying flows. The focus will be on open channel flow dynamics and controlled experimental studies will be designed and conducted to generate and characterise
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respond over time (e.g. changing shape), controlled by the arrangement of differential materials within them. The goal of this project will be to develop responsive 4D-printed biomaterial devices for drug
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-periodic structures, we can precisely control the interaction of radiation with matter, potentially achieving unprecedented timing resolution (sub-70ps) and significantly enhancing signal detection. This PhD