355 machine-learning "https:" "https:" "https:" Postdoctoral positions in United States
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- Zintellect
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Field
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Carlo, GW, and other advanced techniques are oftentimes needed to correctly describe correlated electronic systems. In recent years, electronic structure methods have been coupled to machine learning
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cyclones, the Madden–Julian Oscillation, and atmospheric rivers. We combine theory, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics
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> on research projects related to kinetic equation and its related multiscale model reduction, numerical method, and data-driven/machine-learning approaches, including: • Numerical simulation of kinetic
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will combine first-principles atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data. The associate will develop reproducible
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will combine first-principles atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data. The associate will develop reproducible
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informatics, or related field. • Strong knowledge and experience in natural language processing, machine learning, and deep learning. • Strong written and oral communication skills in English. • Ability to
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 8 hours ago
, computational analysis, machine learning applications), approximation theory, or mathematical models in biology and/or medicine. Teaching duties for the position can be tailored to the applicant’s
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Type IIP supernovae. We use data from current (ZTF, HST, JWST) and next-generation telescopes/surveys (LSST, WFIRST) to address scientific questions using machine learning and statistical methodology
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simulations and machine learning for the development of functional material. Assisting the PI to work on the research project and initiate the new projects and to also provide training assistance for the
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models, causal inference and machine learning. Dr. Liu's research interests lie in modeling the rapidly-accumulating big data (e.g., muti-omics) in biology and medicine for precision medicine via a