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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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relevant to setting a roadmap for ongoing experiments, as well as recently developed applications of tensor network techniques to large-scale partial differential equations. We are advertising two positions
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investigating adaptive neural circuits underlying distinct forms of behavioural flexibility. The postholder will be responsible for designing, developing, and operating advanced large-field or multi-brain region
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processing, integration and visualisation Proven experience developing and using necessary pipelines for analysis of large-scale bulk and single cell data sets Strong understanding of statistical modelling
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Christopher Yau (http://cwcyau.github.io ) at the Big Data Institute, University of Oxford. This post will contribute to the development of a new simulation-based pre-training framework for building more robust
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collaboration with colleagues in the John Radcliffe Hospital and the Oxford Big Data Institute, with the central aim being the development of rapid diagnostics of antimicrobial resistance in clinical samples. You
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We are seeking an exceptional and highly motivated Senior Research Scientist/ Data Analyst with a passion for tumour immunology and strong expertise in large-scale transcriptomic data analysis
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the John Radcliffe Hospital and the Oxford Big Data Institute, with the central aim being the development of rapid diagnostics of antimicrobial resistance in clinical samples. You will work as a member of an
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member of the ‘Blackholistic’ team (Oxford-Amsterdam-Radboud) which includes relativistic simulations on all scales from black hole to large scale jets, as well as analysis of data from the Event Horizon
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project focused on systematically exploring the impact of the exposome on complex disease risk, through the lens of multi-omics data (e.g., genomics, proteomics, metabolomics and biochemistry) from large