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the analysis of crosslinking and immunoprecipitation (CLIP), ribosome profiling and RNA proximity-ligation data (e.g. hiCLIP). We are looking for an innovative, and highly motivated scientist who is keen to
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, Electrical Engineering, or a related discipline. Strong research background in one or more of: Computer Vision Machine Learning Deep Learning Video Understanding Multimodal AI Excellent programming skills in
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strong technical background in flow chemistry and a proven record in the development of complex hardware systems? Are you interested in working in partnership with industrial partners to develop extraction
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. Oversee framework analysis of qualitative data, including development of the analytical framework and interpretation. Develop and lead bids for external research grant funding, collaborating within and
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Organisation: Cranfield University Faculty or Department: Faculty of Engineering and Applied Sciences Based at: Cranfield Campus, Cranfield, Bedfordshire Hours of work: 37 hours per week, normally
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Schemes of Service: Research Division: Engineering Employment Type: Fixed Term As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will
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, Media and Telecommunications (TMT) sector. Building on the insights and datasets developed through the first two phases of the Transfomation research programme, this project examines how TMT organisations
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written outputs. The postholder will be part of the Global Health Economics Centre, a world-leading group of over 100 academics working on a diverse portfolio of health economics research. The successful
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to addresses the main priorities of the engineering net zero mission by accelerating deployment of a new technology that unlocks net-zero chemicals/e- fuels production and offers a route to valorise the CO2
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written outputs. The postholder will be part of the Global Health Economics Centre, a world-leading group of over 100 academics working on a diverse portfolio of health economics research. The successful