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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 6 hours ago
assimilation Integration (JEDI) framework for atmospheric data assimilation Develop machine learning foundation models based on simulated observations from high fidelity atmospheric simulations for developing
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communication skills, in academic as well as non-academic contexts Downloading a copy of our Job Description Full details of the role and the skills, knowledge and experience required can be found in the Job
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 28 days ago
looking for a highly motivated Postdoctoral fellow to work on accelerated molecular simulations and AI-driven drug discovery. Research projects in the Miao Lab include development of novel theoretical and
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, validated through field campaigns. Key Responsibilities Conduct atmospheric chemistry simulations Develop deep learning models for emission predictions Participate in field campaigns across Africa Publish in
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wave theory ᆞAtomistic-spin simulations ᆞMicromagnetic simulations Terms and Application: ᆞStart Date: As early as July 1, 2026. ᆞDuration: One year (initial), extendable for one additional year
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experiments, while performing precise measurements of neutrino-nucleus scattering processes. The successful candidate is expected to play a leading role in simulation studies and data analyses aiming
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. The postdoctoral appointee will work closely with multidisciplinary teams at Argonne and partner institutions and is expected to contribute to both methodological innovation and practical validation using simulation
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on the development of technology for lunar and Martian regolith handling and beneficiation. · Develop and apply modelling and simulation approaches to particle separation systems. · Optimize the design
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both empirical and simulated genetic data. These metrics will be applied to a unique large-scale dataset on plant genetic diversity in grasslands and forests. The position is part of the collaborative
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modelling approaches to simulate plasma-assisted combustion systems. This includes developing and validating detailed phenomenological PAC models, implementing coupled plasma–combustion chemical kinetics, and