115 data-"https:" "https:" "https:" "https:" "https:" "Simons Foundation" Postdoctoral positions at University of Oxford
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Description' for further details on the responsibilities and selection criteria, as well as further information about the university and how to apply. The post is full time for a fixed term until 31st December
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, including overseeing the research database and preparing reports. You will undertake prediction modelling and/ epidemiological research using population datasets including statistical analyses of the data
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learning systems. Reporting to the principal investigator, Professor Christopher Summerfield, the post holder will be a member of the Human Information Processing Lab. They will be responsible for carrying
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a social science discipline (or a relevant data science field), have interest and research in the field of economic and experience in data management and analysis. You have demonstrable experience
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lab has developed the OrthoFinder comparative genomic methods. OrthoFinder has become widely-used in comparative genomics research, it powers many popular databases of online genomic information, and
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-ome data at bulk and single cell resolution. You will hold a PhD in Mathematics, Systems Biology or a related subject. Experience with mathematical modelling of dynamical systems using linear and non
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immediately until 31st March 2028, with the possibility of extension. The post is part of a Wellcome Trust Collaborative grant ‘Harnessing epidemiological and genomic data for understanding of respiratory virus
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contribute to publications and grant applications. Please see the below 'Job Description' for further details on the role, responsibilities, and selection criteria, as well as further information about the
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glial models, multi-electrode array (MEA), and multi-omic profiling to dissect neuroimmune interactions. You will lead experimental design, data generation, and integration across molecular and functional
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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