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intelligence. Our research spans computer vision, machine learning, and natural language processing, focusing on multimodal learning, data fusion, spatial-temporal modeling, and vision–language models. We study
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initiatives. OR Doctoral degree in a quantitative discipline with demonstrated expertise in statistical modeling, machine learning, data science, research methodology, or related analytical fields. Required
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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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the development and application of quantitative methods for traffic-safety analysis. The research will combine empirical crash and traffic data with statistical modelling, machine learning and traffic or driving
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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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related to modeling and simulation of biological systems, 3) very good IT skills, in particular the ability to program in Python, 4) very good knowledge of machine learning methods, neural networks, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 8 days ago
of proficiency in either R or Python in the areas of machine learning, deep learning, statistical analysis, computer vision, and/or graph analysis Experience with data engineering to create data
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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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with health/real grid data or machine learning is beneficial but not mandatory. We Offer One year funded of 3-year PhD position (SIF Doctoral Student Grant). Access to high-performance computing
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following projects: Project #1 Title: Enhance Students' Interdisciplinary Learning through Large Language Model-Empowered Learning Analytics (MOE TRF 2/25 ZG) Project #2 Title: Building an Eco-system