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Field
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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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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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culturing, pathogenicity assays, molecular biology, genome sequencing and genomic analyses. Experience in field-based assays would be beneficial as large-scale disease screening is planned for March 2026
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research; Are excited to support a collaborative project, and contribute to the wider objectives of a translational research team; Have research experience handling large human genetic data with an interest
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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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, funded by the UKRI Frontier research grant titled “StochFields”. They will be expected to conduct research which falls within the remit of this large-scale project and will have the opportunity to do so in
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, funded by the Simons Foundation grant titled ‘Simons Collaboration on Probabilistic Paths to Quantum Field Theory’. They will be expected to conduct research which falls within the remit of this large
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-based assays would be beneficial as large-scale disease screening is planned for March 2026. The jobholder will be expected to document and maintain lab-records to a high standard. Occasional travel will
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for analysis of large-scale bulk and single cell data sets Strong understanding of statistical modelling, data normalisation and machine learning methods applied to biological datasets Experience with data
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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