2,351 machine-learning-"https:" "https:" "https:" "https:" "https:" "UCL" "UCL" Postdoctoral positions in United States
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information, national origin, race, religion, (including pregnancy and pregnancy related conditions), sexual orientation, or military status. Duke aspires to create a community built on collaboration
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. Responsibilities: • Design and conduct experimental research on paper-based materials for food packaging applications. • Develop predictive models for packaging performance using machine learning and physicochemical
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transduction, or protein-protein interaction. Computer skills in data and statistical analyses are required. A proven record of productivity as demonstrated by recent first author publications in leading peer
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 4 hours ago
receive guidance from a mentor and will learn about conducting research analysis within OP. You will engage with current OP staff on policy analysis projects and assignments in one of the following focus
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-principles and atomistic simulations with machine-learned interatomic potentials to: Model reaction pathways on metal-oxide surface, including adsorption, reactions and diffusion steps. Construct atomistic
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work to exciting research in multi-disciplinary domains alongside globally recognized experts. You will bring creative thinking, teamwork, and machine learning skills to bear as you develop new methods
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | 2 months ago
to advance use of foundation models for Earth Science research and applications, including fine-tuning experiments Use of remote sensing, models, and/or machine learning to further our understanding
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status, sexual orientation, military/veteran status, gender identity, or other non-merit factors. If accommodations are needed for completing the application and/or with the interviewing process, please
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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, genetic information, national origin, race, religion, (including pregnancy and pregnancy related conditions), sexual orientation, or military status. Duke aspires to create a community built on