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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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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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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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-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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automated rankings. The research includes real-world validation using university admissions data and will contribute to the broader fields of AI in education and ethical decision-making. This PhD 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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recycle content crush alloys. The main objective of the project is to understand the deformation behaviour of the high recycle content crush alloys and the role of tramp elements in controlling the final
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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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control. Joining the leading researchers in the Centre for Engineering Research at the University of Hertfordshire, collaborating with our industrial partners and becoming a member of the vibrant doctoral
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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