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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
in computational biophysics/chemistry and drug design. The researcher will conduct research to forward the developments of biomolecular modeling and computer-aided drug design. The researcher will also
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
biophysics/chemistry and drug design. The researcher will conduct research to forward the developments of biomolecular modeling and computer-aided drug design. The researcher will also be engaged in developing
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
computational biophysics/chemistry and drug design. The researcher will conduct research to forward the developments of biomolecular modeling and computer-aided drug design. The researcher will also be engaged in
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clinical features using machine learning and foundational modeling approaches. This work supports disease modeling across chronic kidney disease, acute kidney injury, cancer, and neurological conditions. A
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learning–based), advanced mesh generation techniques for simulation, and experience with biomedical simulation, both virtual and physical. Experience with laboratory and clinical validation of models is
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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
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on physics analysis and artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision Higgs and Standard
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Vision, with focus on multimodal learning; Deep generative models, e.g., GANs, diffusion models, encoder-decoder architectures, optimal transport models, Flow Matching; ML and CV approaches with a
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Postdoctoral Research Associate will be responsible for AI-driven materials discovery. The position will collaborate with interdisciplinary teams to develop integrated molecular simulation and machine learning
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grasslands and evaluation of land-use intensity, Expertise in classification with machine-learning methods, statistics, spatial analysis and land-use modeling, Experience and interest in conducting fieldwork