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. Position Overview The successful candidate will develop and apply advanced computational and machine learning methods to large-scale genomic, clinical, and imaging datasets, working across one or more of the
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. Education and scholarly development The postdoctoral associate will receive structured education in computer vision applications in medical imaging, machine learning, research methodology, responsible conduct
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include: Biomedical sensing and physiological monitoring Edge intelligence and energy-efficient machine learning hardware Radar and wireless signal processing and communications The successful candidate
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skills, including generalized linear models, multiple machine‑learning algorithms, MOFA and multi‑omics pathway analysis. · Strong background in experimental design, quantitative data analysis, and
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other techniques to ensure the records are well connected and well described. Alignment of natural history datasets at the data rather than metadata level Using the LUX paradigm and code to construct a