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for this position will work as a member of an interdisciplinary team led by Dr. Colin Xu on Department of Defense (DoD)-funded research project involving the use of statistical and machine learning methods
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
. Throughout the Fellowship, individuals will have the opportunity to participate in research using digital pathology, machine learning, deep learning, artificial intelligence, and digital image analysis. Other
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position in biomedical informatics is available at Harvard Medical School to work at the intersection of advanced machine learning and large-scale biomedical data. The selected fellow will join a dynamic
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positions in the area of Artificial Intelligence (AI) and Machine Learning (ML) in Drug Discovery. This is a unique cluster hire initiative spanning the College of Pharmacy, Life Sciences Institute (LSI), and
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assessment, programming and machine learning. If so, we encourage you to apply! You will develop exposure and physical vulnerability maps for past and future (1970-2100) and integrate these into a flood risk
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, computational neuroscience, bioinformatics, robotics, or a related field Strong expertise in computational data analysis (e.g., behavioral analysis, signal processing, or machine learning) Experience working with
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related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with transformer architectures (e.g., ViT
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solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/human-organs-on-chips/ What you’ll do: Independently
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- 4 Additional Information Eligibility criteria • Experience in computer modeling and programming • Knowledge of associative learning at both the neurobiological and psychological levels • Experience
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights