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Help shape the storage infrastructure that enables researchers across the University to work securely and effectively with large-scale, data-intensive computing. This is an exciting opportunity
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of running at scale within high-performance computing (HPC) environments. In parallel, the postholder will undertake the bioinformatic analysis of NGS datasets, generating the data and insights required
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parameter inference techniques. The role will also involve creating robust, efficient, and reproducible computational code capable of running at scale within high-performance computing (HPC) environments. In
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, and for the computational analysis these data demand. We encounter thousands of chemicals over a lifetime, and for the overwhelming majority, the biological consequences are unknown. Where there is an
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mathematics, and knowledge in deep learning. Practical experience in a broad range of techniques including LLM training, evaluation, RLVR, PEFT, quantisation, tensor/data parallelism. Ideally, familiarity with
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uncertainty. In parallel, the activity of specific neuronal subtypes will be measured. These findings will inform a novel theoretical framework which will be iteratively refined by experiments to selectively
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of error and uncertainty. In parallel, the activity of specific neuronal subtypes will be measured. These findings will inform a novel theoretical framework which will be iteratively refined by experiments
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experience of working with children and young people, and could be held in parallel with an existing clinical position. Whilst the primary focus of the posts will be teaching on the EMHP programme, there may
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involve testing activity against candidalysin variants from clinically relevant Candida strains and species, helping to define breadth of protection. In parallel, the researcher will support compound
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the UK must close, and turning that into the knowledge infrastructure a national programme runs on. A research theme of your own is developed alongside that work, not instead of it. This post focuses