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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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integration of novel computer architectures for advanced edge computing scenarios. The research involves design space exploration and the implementation of digital systems using FPGAs, with potential for future
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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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these areas commensurate with career stage. Experience of FORTRAN and python programming. Previous experience in developing and using parallel computer codes using the Message Passing Interface (MPI). This is
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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