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related field, and will have experience with cell-free protein expression systems, protein engineering, high throughput screening and lab automation. Extensive experience with recombinant protein expression
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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 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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-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
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region and identify mutations. Develop and optimise bioinformatics tools to detect mutations using positive controls. Apply polygenic risk scores (PRS) to genome-wide SNP data to identify individuals
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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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-printed functional devices interact with their environment, responding to stimuli (temperature, light, etc.), and “4D-printed” devices respond over time (e.g. changing shape), controlled by the arrangement
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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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, 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