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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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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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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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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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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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, 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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Department. The selected candidate will have access to the HCP’s large consortium of world experts in multiple neuroimaging domains and ample opportunities to publish research methods and scientific findings
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be involved in projects to perform big-data research, bundling genetics, multi-omics, biomarker, clinical, and histopathological data, to harness the full range of biologically relevant data to better
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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