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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours ago
of Computer Science at the University of North Carolina at Chapel Hill seeks a talented, highly motivated postdoctoral researcher with a background in machine learning and/or biomedical image processing. The ACM Lab is
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for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins. There will be potential
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. Preferred Qualifications • Experience with deep learning architectures applied to geophysical or environmental data. • Familiarity with physics-informed machine learning or hybrid modeling approaches
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Associate selected for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins
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simulations), computational chemistry, machine learning, enhanced sampling methods, force field development, or related fields are encouraged to apply. Start date is flexible. the postdoc will collaborate with
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Children's Cancer Center. The Postdoctoral Associate will finish experiments, data analysis, and manuscripts related to the IL18 mediated enhancement of CAR NKT anti-tumor activity project. The ideal candidate
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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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models and machine learning techniques for kinetic equations arising from plasma and neutron transport. The position will be based at Virginia Tech’s campus in Blacksburg, VA. The postdoc will have a
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, high-performance computing (HPC), dimensionality reduction, and machine learning. Your responsibilities will include publishing research in leading conferences and journals, contributing to grant
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scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning