1,489 modeling-and-simulation-"UNIVERSITY-OF-SOUTHAMPTON" Postdoctoral positions in United States
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Field
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for simulating river network dynamics, such as R, Julia, Python, or GIS-based hydrological modeling platforms. Ability to integrate physical, chemical, and biological components into the river-lake network models
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Monte Carlo charged particle simulations to model energy deposition and generation of secondary particles, and collaborate with a team of researchers to explore heating, melting, vaporization, and general
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 8 days ago
numerical approaches (e.g., Large Eddy Simulation, Direct Numerical Simulation, multi-scale process modeling) and laboratory wave tank experiments to explore turbulent seawater dynamics adjacent to ice
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extensive experience developing electrochemical models and simulating battery performance. Relevant publications are available at his Google Scholar page . Applicants do not need to be US citizens. The start
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Postdoctoral position in AI for protein design with applications to TCR & BCR models The lab of Prof. María Rodríguez Martínez at the Department of Biomedical Informatics & Data Science, Yale School
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at conferences Required Qualifications • Ph.D. in Chemical Engineering, Materials Science, Chemistry, or related field • Experience with computational modeling and simulation of polymers • Record
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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. The goal of this work is to investigate the dynamics of beams with intense space charge and benchmark simulation models against experimental results. As a U.S. Department of Energy (DOE) Office of Science
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migration. Support integration of defect models into existing TCAD or custom simulation platforms. Collaborate with experimental and atomistic modeling teams to inform model inputs and validate results
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understanding of geomagnetic field evolution across different timescales, including both stable and extreme periods. This will involve working with data-based models and numerical dynamo simulations