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Field
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contributing to multiple projects including resilience-aware scheduling, deep learning workload job scheduling, and storage system performance tuning. The candidate will have the opportunity to engage in
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opportunity promises deep academic inquiry, industry collaborations, and the chance to influence future practices and research directions in these fields. Key Responsibilities: Develop robust mathematical and
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information, remote sensing data, and GIS software. Experience in deep learning and computer vision. Experience in developing software tools and products. Experience in writing scientific papers and successful
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 13 days ago
/missing data, and interpretable deep learning. The duties of the postdoctoral scholar will include aiding the research of graduate students on multimodal ML projects and leading research directions
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discipline. The candidates will have expertise in computational imaging, with: (i) an algorithmic focus, with particular interest in methods at the interface of deep learning and optimisation theory, and/or
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a 3-year DOE-sponsored project that started in September 2024. The Postdoctoral Research Associate working on P1 will develop and test deep learning algorithms for model emulation and model parameter
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-Based Wildfire Smoke and Air Quality Monitoring; Deep Learning for Post-Wildfire Damage Assessment. PROFILE of the OFFICE OF POSTDOCTORAL AFFAIRS (OPA) The mission of the UNLV Office of Postdoctoral
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Post-Doctoral Position - Medical Image Processing - (240000Y9) Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in
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research in the field of machine learning, more specifically, deep learning and representation learning architectures. Application areas of ML include, but are not limited to, computer vision, natural
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- Norman Campus (Norman, OK) Open Date Apr 05, 2023 Description The candidate is responsible for analyzing and mining multi-omics data using computational techniques, such as deep learning, generalized