327 modeling-and-simulation-"UNIVERSITY-OF-SOUTHAMPTON" Postdoctoral positions in United Kingdom
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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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and/or flow cytometry-based readouts of infection Experience with biochemical assays (e.g. Western blotting) Experience handling/processing samples in vivo infection models and/or human samples
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and experience: Essential criteria PhD in bioinformatics, computational biology, or a related discipline * Extensive experience and expertise in analysing/ training models on biological or chemical
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models and/or human samples Experience with eukaryotic cell culture systems Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found
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vitro and in vivo infection models, and both microscopy and flow cytometry-based readouts of infection to develop their independent research project and will also be expected to collaborate more widely
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of tumour mouse models and assess the antitumour effect of this therapeutic method. The candidate should have the skills to develop patient derived organoids for orthotopic injection in mice. The candidate
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- Develop original numerical methods for facility simulation in presence of expansion waves - Demonstrate improved estimates of rate constants for two-temperature models - Contribute
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and/or flow cytometry-based readouts of infection Experience with biochemical assays (e.g. Western blotting) Experience handling/processing samples in vivo infection models and/or human samples
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dedicated to development, translation and clinical application within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical