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Title: Predicting and Improving the Quality of Recycled Plastics Using Advanced Metrology and Data Science Research theme: "Materials Characterisation" "Data Science and Machine Learning in
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enhancers at repetitive GGAA DNA elements (GGAA microsatellites). These neo-enhancers appear to contribute to tumorigenesis and eventually tumour progression and possibly underlie germline variation
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environmental stressors, in particular heat, and patterns of violence in the UK. This project will largely use quantitative methods to explore the relationship between climate change and violence. A key component
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We are seeking a highly creative and motivated Postdoctoral Research Assistant/Associate to join the Machine Learning Group in the Department of Engineering, University of Cambridge, UK. This
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The growing demand for high-performance, low-power large-area CMOS technologies has driven interest in a range of new materials, including silicon-based thin films, organic semiconductors, and metal
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We are looking for a highly motivated candidate with strong interest in catalysis and hydrogen economy to take up this exciting PhD project. You will collaborate with other postdoctoral researchers
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. While a background in materials science is preferable, it is not a prerequisite, as there are opportunities for retraining. This ensures that the project is accessible to individuals from diverse academic
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Application deadline: All year round Research theme: Computational Chemistry, Material Science No. of positions: 1 Eligibility: UK students This 3.5-year project is fully funded by industry and home
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digital twins, and life cycle assessment (LCA). A central component of the research will be the development of digital twins to simulate the entire production process, from raw materials to final product
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musculoskeletal conditions. Apos utilizes personally calibrated footwear with unique biomechanical elements attached to the sole of the shoe that alter the user's walking pattern, redistributing forces across