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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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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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, design and analysis of virus-derived RNA libraries, and development of machine learning models for detecting functional elements in viral metagenomic datasets. This project is a collaboration with the
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The postdoctoral associate will be embedded in a collaborative research environment that brings together expertise in functional genomics, regulatory genomics, machine learning, population genetics, and human
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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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epidemiologic and statistical methods, including causal inference approaches, machine learning techniques, and high-dimensional data integration methods. Prepare first-authored manuscripts, abstracts, progress
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-scale human datasets. You will: - Build and apply machine learning and deep learning models to multi-scale (cells, brains, patients), multi-modal (omics, biosensor data, vision, electronic health data
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platforms a plus Experience applying machine learning, artificial intelligence, and large language models to research a plus The anticipated start date is September 1, 2026. The postdoctoral position incoming
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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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cell immunology, mucosal immunology, and cancer immunology using animal models and single-cell multiomics/spatial transcriptomics approaches. This postdoctoral researcher will combine wet-lab molecular