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(SMEs) in the health and life sciences (H&LS) sector to embed and accelerate sustainable manufacturing in their businesses. Integrated research in this programme will generate evidence-based guidance
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formation of peroxynitrite determined using a phenylboronic acid spin trap and liquid chromatography-mass spectrometry.[4] This project will make extensive use of green chemistry techniques to address key
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, financial technology, policy analysis, or academia. Ideal candidate: Background in computer science, data science, finance, economics, or related quantitative fields. Strong programming skills (Python/R
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academic papers in top AI and finance venues. We welcome applicants with backgrounds in computer science, artificial intelligence, or computational modeling. Skills required of the applicant: Essential
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in Computer Science / Artificial Intelligence or related discipline. Experience of working with sensors. Strong interest in carrying out innovative research in a health context. The successful
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engineering/electronics engineering, or a related field will be advantageous, along with knowledge of the following: *low-power circuits/system design for ASIC, *Verilog based FPGA based system design
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there is less in the way of support for changes in research laboratories. The Microbiology Group in the School of Biomedical Sciences is the ideal test-bed to investigate procedures and approaches. It is a
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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in
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lack of concrete, actionable frameworks to support researchers in adapting more sustainable research practices (Science Europe, 2024). The proposed MRes project seeks to address this issue, focusing
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experts, building a powerful, dual-skill profile. Completing this project will establish you as a leading expert in industrial AI and computer vision, highly sought after in the growing Smart Manufacturing