143 parallel-computing-numerical-methods-"Prof" Postdoctoral positions at University of Oxford
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and social data with the aim to better understand energy use. The programme is an interdisciplinary collaboration between the School of Geography and the Environment (Oxford), the Department
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expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will focus on ambitious, ‘blue sky’ research for novel methods development
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the UKRI through the Frontier Guarantee Programme to Dr Jani R Bolla. The work is to be conducted in his lab in the Department Biology, University of Oxford, South Parks Road, Oxford, OX1 3RB
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science, with applications to public and veterinary health, therapeutic development, and pandemic preparedness. You will contribute to method development, data analysis, and collaborative projects, and will
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part of a wider programme of work to establish that membraneless organelles, biological liquid droplets, are effectively regions of organic solvent, suspended inside cells and that the properties of each
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in the Department of Chemistry, University of Oxford, for a period of up to 3 years. The project involves the development of methods to use light to regulate transport of amino acids and to engineer
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the tree of life. The main responsibilities will be to identify ancient gene families that encode membrane proteins and then use a range of phylogenomic methods to understand their ancestry. These analyses
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discoveries on the electrosolvation force. The project will use a range of optical methods to examine the interactions in colloidal and molecular systems and relate the experimental findings to theories
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on single-agent settings. We are seeking a highly motivated postdoc to conduct research into this fast-moving area. Directions may include investigating quality evaluation methods for multi-agent systems
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cryo-EM, cryo-ET, FIB milling, and biochemical methods to understand mechanisms of dynein-2 transport, building on recent advances in the group (NSMB 2017 PMID 28394326; NSMB 2019 PMID: 31451806; Cell