30 parallel-computing-numerical-methods Postdoctoral positions at KINGS COLLEGE LONDON in Uk
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United Kingdom Application Deadline 21 Sep 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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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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with in vitro and intracellular persister assays, including CFU/survival-based methods and macrophage infection models, with a demonstrable publication record on Salmonella Typhimurium antibiotic
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-legal Studies, Science and Technology Studies (completed, or to be completed before post start date), or a cognate field. Experience of qualitative archival or interview-based research methods in a
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their relationship with treatment responses and disease activity. This is an excellent opportunity to contribute to a collaborative research program dedicated to improving our understanding of ALS and informing future
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Institute and Centre Lead for Cancer Genomics and Computational Biology at Barts Cancer Institute, Queen Mary University of London). We welcome applications from all qualified candidates. Research staff
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novel therapeutic strategies, and for anticipating the evolutionary trajectories of other viruses with pandemic potential. Our current research programme is structured around three central themes
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schizophrenia-related symptoms in animal models (mice), in the context of a collaborative project with clinicians and computational scientists. This project will be supervised by Prof Oscar Marin and Prof Beatriz
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for more information. About you To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD degree in Engineering, Computer
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development laboratories at Guy’s Campus, London Bridge. The group specialises in inventing custom fluorescence-lifetime and multiphoton technologies and coupling them with powerful computational pipelines