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developing characterisations of network models and interactions with methods in statistical machine learning. The post holder provides guidance to junior members of the research group including project
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University of Cambridge, Department of Pure Mathematics and Mathematical Statistics Position ID: CambUK -RESEARCHASSOCIATE [#26302] Position Title: Position Type: Postdoctoral Position Location
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challenging analyses, overcoming statistical problems, producing high-quality visualisations and outputs, and delivering reproducible analysis scripts. You also need a strong track record of delivering high
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crops and soil. Skills in environmental modelling and statistics. A positive record in scientific publication / report writing. Evidence of strong team working in research setting. Industry and
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with experimental collaboration to uncover complex biological mechanisms. Our interdisciplinary work draws on statistical physics, applied mathematics, and close ties with experimental labs. Current
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computer programs to design experimental paradigms, analyse data and conduct advanced statistical analysis. Prior experience in running neuromodulation studies including TMS and TUS is essential. You will be
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Machine Learning, Human-Computing Interactions, Social Sciences, and Public Health. Applicants should hold, or be close to completion of, PhD/DPhil with research experience in computer science, statistics
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
Experience in uncertainty quantification or statistics applied to quantum chemistry and machine learning would be advantageous For more details, please take a look at the role profile. We'll still consider
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collaboration with statistical physicists for data analysis and experimental design. The Associate is expected to generate breakthrough ideas in the assigned area of research, as well as to carry out research in
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at the intersection of these research areas. You should hold, or be close to completing, a PhD/DPhil in mathematics, statistics, physics, engineering, data science, or a related field. Experience in cancer