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for analysis of health record data for patient diagnosis and outcome prediction. Perform large-scale querying and analysis of clinical health record databases. Engage with clinical collaborators, to place 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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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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projects. Fellows may pursue projects that utilize the CIP archive of large-scale social media data, as well as design and execute new data collection efforts that utilize existing research infrastructure
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genomic tools and protocols for large scale genetic mark recapture (GMR) programs in Atlantic Bluefin Tuna (Thunnus thynnus) in the northwestern Atlantic. The project is a collaboration among Lorenz Hauser
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of the appointment. Demonstrated experience with deep learning methods or sophisticated mathematical frameworks applied to large-scale or scientific datasets. Experience working with observational seismology data (e.g
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interested in digital mental health research. We are looking for individuals with a strong commitment to data-driven digital assessment, monitoring, and treatment of serious mental illness. Individuals with
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, Greninger Lab Position Details Position Description The Greninger Lab at the University of Washington is seeking a Postdoctoral Scholar driven by curiosity, eager to dive deep, think big, and learn fast - a
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aiming to investigate parent infant interaction with a focus on music and speech at the University of Washington Seattle Campus. This job focuses on analyzing a large longitudinal dataset (video-audio
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accuracy in link-tracing designs (e.g. Respondent driven sampling) Partial graph data collection strategies for networks (e.g. Aggregated Relational Data) Large scale models for anomaly detection on graphs