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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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security guarantees. In parallel, we will investigate how modern AI techniques can accelerate large-scale formal verification, developing AI proof agents that can maintain, extend and refactor the seL4 proof
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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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architecture of entirely new foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across
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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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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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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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empirical evidence. The project runs for 33 months and comprises three research components operating in parallel, each led by a postdoctoral researcher and integrated through monthly team meetings and
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systems and has developed the best-in-class large scale testbeds for research and innovation and enjoys the state-of-the-art lab and computing facilities. Established in 2012, 5GIC is the world’s first