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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
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part-time post (0.2FTE) ideal for someone working in industry or with industry experience. This is because we want to bring in expertise with data processing and machine learning pipelines, and their
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will work as a member of an interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine
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, learning under uncertainty) that is of an international standard, and that is carried out expertly, rigorously and in accordance with ethical guidelines. You will also participate actively in the lab
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-in-cell computer codes hosted on local and national high-performance computing clusters; establishing all-optical diagnostics to map temperature evolution in plasma accelerators; exploring novel inter
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proof-of-principle repetition-rate and staging experimentation. The successful candidate will perform duties that include developing/using particle-in-cell computer codes hosted on local and national high
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interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine their antibiotic resistance. Your work will
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collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
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pandemic risk policy and practice. The PDRA will drive a research project within the centre focussed on the application of machine learning and bioinformatics approaches to the prediction of pandemic risks
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in probability, stochastic analysis, and optimization. Applicants with knowledge in machine learning, as well as a track record of publications in top Actuarial Science, Mathematical Finance