20 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" Postdoctoral research jobs at University of Washington in United States
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The Department of Biostatistics at the University of Washington has an outstanding opportunity for a postdoctoral scholar. The postdoctoral scholar will develop statistical machine learning and artificial
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a novel multi-omics approach that integrates high-throughput imaging and machine learning methods with CRISPR/Cas9 screens and saturation mutagenesis to answer central questions about the
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or more projects, learning advanced cellular and molecular biology and anaerobic microbiology techniques. The candidate’s day will be split between benchwork to generate data, and computer work to generate
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blood samples to advance patient care. This role will involve developing computational models (statistical, machine learning, etc.), and using them to perform high throughput analysis of clinical data
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morphology (e.g., geometric morphometrics, machine learning), and phylogenetic comparative approaches. We have: • An engaging, supportive, and collaborative research environment. • Opportunities
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candidates with strong expertise in Bayesian methods, uncertainty quantification, and/or machine learning applied to nuclear theory. The group’s research spans a wide range of topics including nuclear
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include experience with fiber sensing, machine learning tools, and big data workflows. Instructions To apply, candidates will submit materials via Interfolio, comprising (1) a letter of interest describing
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computer simulations, as well as prior work with food and other biomaterials. The application deadline is December 15, 2025. Interested applicants are encouraged to contact Juming Tang (jutang88@uw.edu
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visitors from all over the globe who come to learn, study, teach, and discover. FHL is committed to fostering an environment that is professional, ethical, inclusive and respectful of all who participate in
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, atmospheric signals), data fusion across sensing modalities, and development of scalable machine learning pipelines. Work will be entirely computational and based in Seattle, with no field deployment