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conferences and journals, specifically in Computer Vision and/or Natural Language Processing Proficiency in python and deep learning libraries (pytorch) Proficiency in git, Docker, python, and SQL Experience in
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intelligence. Our research spans computer vision, machine learning, and natural language processing, focusing on multimodal learning, data fusion, spatial-temporal modeling, and vision–language models. We study
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: Sitting using near vision use for reading and computer use for extended periods of time. Lifting (approximately 20 to 30 pounds), bending, and other physical exertion. As part of your application, we
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
Posting Information Posting Details Department Physics and Astronomy - 319000 Posting Open Date 09/25/2026 Application Deadline 03/23/2027 Open Until Filled No Position Type Postdoctoral Scholar
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Sign In Create Profile Postdoctoral Research Associate I United States | req27098 Apply Now Share Save Job Back to Search . Postdoctoral Research Associate I Posting Number req27098 Department
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in collaboration with local and international partners. The Postdoctoral Associate will design, execute, and interpret computational analyses for large-scale genomic, transcriptomic, and epigenomic
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be close to completion / have equivalent postdoctoral level experience) in a relevant area. Wet-lab experience of preparing samples for next-generation sequencing, experience of conducting research
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statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision
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Job Title Postdoctoral Research Associate Agency Texas A&M Agrilife Research Department Soil & Crop Sciences Proposed Minimum Salary Commensurate Job Location College Station, Texas Job Type Staff
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at the University of Arizona is seeking a postdoctoral research associate to develop theoretical and computational approaches for understanding how biological neural networks organize activity through nonlinear