35 computer-science-data-wahrn-hous Postdoctoral positions at University of Cambridge
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of the research programme is to develop EHR common data model specifications and to advance knowledge in the field of psychiatry EHR research, including clinical risk prediction modelling. The appointee will work
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at the Yusuf Hamied Department of Chemistry, University of Cambridge to work on the BBSRC grant "A Platform for Identifying GlycoRNA and Identifying Biases in RNA Pulldown". The role is to develop methods
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involves computer simulations of catalytic and environmental interfaces, aiming at reaching fundamental new understanding of elementary processes at such interfaces. As part of our work, we also seek
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bioinformatics/computer science will be essential. Prior experience with connectomics data is highly desirable. Our group has developed an international reputation in this area and our tools have now been used in
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P. Grey in the Yusuf Hamied Department of Chemistry, working on a project focused on understanding solid-state ion dynamics and phase transformations in lithium- and sodium-ion battery electrode
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Meier, Charo Sadaba). You will be responsible for completing research projects fitting computational models to social media behavioural data (e.g., see https://doi.org/10.1038/s41467-020-19607-x
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information. http://www.proroklab.org The Department of Computer Science and Technology is an academic department that encompasses computer science along with many aspects of engineering, technology and
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. This role offers a rare opportunity to contribute to cancer interception science at the frontier of personalised prevention, with strong translational potential. We are seeking a talented and motivated
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desire to learn others: molecular biology, C. elegans or other model organism research, confocal imaging, computational analysis, preferably in python and electrophysiology. We are also looking for someone
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-generation AI hardware (ASIC) accelerators. The UK's Advanced Research Invention Agency (ARIA) is supporting an ambitious programme of work that aims to reduce the the cost of AI by more than 1000x: https