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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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modalities in the rodent brain (3D light-sheet microscopy, spatial transcriptomics and MRI of the same brain) to learn MRI contrasts that can depict neuronal and glial cell density and morphology. You will
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of solid-state materials, with experience in density functional theory (DFT) and/or machine learning interatomic potentials. We welcome applicants with a broad range of research interests and experiences who
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex network models to partially observed hospital infection data, leveraging patient movement data. This research will
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relevant background in information extraction, entity resolution/entity linking, machine learning, uncertainty modelling, explainable AI, or a closely related area, with a PhD (or near completion) in
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Principal Investigator and a cell-culture specialist in a friendly, multidisciplinary group spanning optics, electrophysiology, microfabrication and machine learning, collaborating with partners
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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, implementing, and optimising advanced AI algorithms, with deep proficiency in machine learning architectures, scalable model development, and high-performance code. The role holder will have the opportunity
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, organised researcher who can evidence: A PhD, or equivalent in statistics, machine learning or a closely related discipline, OR near to completion of a PhD. Expert knowledge of statistical inference methods