21 postdoc-computational-fluid-dynamics Postdoctoral positions at University of London
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motivated computational Postdoctoral Research Assistant to lead on an established and successful research line aimed at understanding the genetic events that drive cancer evolution. We have a long-lasting
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About the Role The purpose of this role is to provide qualitative and quantitative research support for a research and impact programme on food reformulation. This role sits within the Research and
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About the Role We are looking for a Postdoctoral Research Assistant to work with Dr Chema Martin on a Human Frontiers Science Program Research Grant project entitled “Evolutionary Biophysics
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal
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related to gravitational wave astronomy. The primary aim will be the development of advanced approaches for computational Bayesian Inference to measure the properties of Compact Binary Coalescence signals
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have a PhD and track record in either computer science with specialisation in relevant AI technologies for surrogate modelling, or in Earth or Environmental Science with a strong track record in
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at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing
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with Prof. Heath Murray at Newcastle University. The group has routine access to the Titan Krios microscopes at LonCEM and eBIC and an in-house computing infrastructure for data processing. About You We
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About the Role The purpose this post is to support the evaluation of a social needs screening programme in East London, development of Living Maps of What Works to address inequalities in primary
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Research Council’s (AHRC) Bridging Responsibilities AI Divides (BRAID) programme that will explore new technologies, new business models and new approaches to data provenance in pursuit of an equitable