27 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" research jobs at University of Washington in United States
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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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of clinical research to ensure adherence to protocols and quality of information received. Must have strong attention to detail, organizational and interpersonal skills, computer skills, and ability
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to the overall mission of our research. Desired Qualifications: Track record of success in co-authorship on scientific papers, presenting results, and representing research at meetings. Knowledge of machine
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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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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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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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, 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