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convection. The objective is to develop reduced but realistic models of cumulus life cycles that may be applied toward cumulus parameterization and/or machine-learning algorithms for predicting short-term
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About the Role We are seeking a Postdoctoral Research Associate in Machine Learning for Cardiovascular Digital Twins to join a team working on the Precision Health, Cardiovascular Devices and Trials
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analysis, machine learning, mathematics of data scienceMathematical modeling and manifold learning of high-dimensional data geometry, with theoretical foundations and applications in evolutionary biology and
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will join a research group working on longitudinal models of multiple chronic diseases across the life course. The postdoctoral researcher will contribute to building and evaluating machine learning
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
experience with a modern deep-learning framework such as PyTorch or TensorFlow. Experience developing, training, and evaluating machine-learning models using complex biomedical, imaging, or other high
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
experience with a modern deep-learning framework such as PyTorch or TensorFlow. Experience developing, training, and evaluating machine-learning models using complex biomedical, imaging, or other high
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learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: implementation of signal processing
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Chemistry to conduct fundamental and applied research at the intersection of machine learning and computational chemistry. In this role, you will develop physics-informed, symmetry-aware models
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Cognitive Science, Medical Informatics, or a comparable quantitative discipline Proven hands-on experience working with LLMs, foundation models, multimodal AI systems, or related machine-learning technologies