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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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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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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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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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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
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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
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Position Overview School / Campus / College: College of Engineering Organization: Electrical and Computer Engineering Title: Research Assistant Professor (Non-Tenure) - Li Lab Position Details
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://postdoc.wustl.edu/prospective-postdocs-2/ . Trains under the supervision of a faculty mentor including (but not limited to): Learn to conduct brain imaging research with surface-based analyses methods and the HCP
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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