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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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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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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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. · Strong background in machine learning/AI and hands-on experience with large, heterogeneous datasets. · Practical experience with computer vision and/or spatio-temporal modeling (object detection
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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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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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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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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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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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computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate hydraulic flood