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Field
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related discipline Required Other None Additional Preferred Experience working in one or more of the following areas: Longitudinal data analysis Predictive modeling/machine learning models Biostatistics
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following: life cycle assessment, life-cycle cost analysis, pavement simulation, machine learning, deep reinforcement learning, and/or physics-informed modeling frameworks; and demonstrated ability to
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: Machine learning/predictive modeling for longitudinal data analysis or large language models. Strong motivation in the development and application of digital twins, particularly in health and biomedical
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 12 hours ago
integrating mechanistic modeling and machine learning methods to analyze and predict drug properties, patient responses, and benefit-risk profiles, using real-world and regulatory datasets. This
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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combining state-of-the-art machine learning with physicochemical knowledge and molecular modeling. Representative publications from our group include: https://doi.org/10.1038/s41467-025-63947-5 https
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machine learning, reduced-order modeling, or data-driven modeling of physical systems. You will conduct research on the development of fast, trustworthy, and data-efficient surrogate models that
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University of California, San Diego | Estancia de San Diego, Guanajuato | Mexico | about 2 hours ago
• Develop and apply reduced-order modeling approaches using spatiotemporal decompositions of large high-resolution simulation datasets. • Develop and train machine learning architectures using reduced
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learning-assisted methods: data assimilation, uncertainty quantification (UQ), and machine-learning-based moment closure models. · Multiscale modeling: kinetic limits of many-particle dynamical systems
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conduct collaborative research at the intersection of non-canonical kinetic theory, non-conventional plasma turbulence modeling, high-speed flows, scientific machine learning for multi-physics problems