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—to macroscopic, population-level observables in rapidly evolving pathogens such as SARS-CoV-2 and influenza. Concretely, you will: formulate and analyze stochastic models of evolving populations using methods from
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system of the solar interior and the solar atmosphere using a combination of theory, simulation, and data analysis investigations. Theoretical studies and computer modeling of the internal structure
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) generating models of the atmospheres of discovered exoplanets and performing retrievals to aid the interpretation of atmospheric observations; (5) performing simulations of transiting exoplanets with Roman and
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by Renew Risk Ltd. (https://www.renew-risk.com/ ), offering opportunities to work with real offshore wind farm models and industrial datasets while addressing real-world challenges in collaboration
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rare-earth ions, with improved quality for optical, magneto-optical and laser applications. Different growth methods will be used such as Czochralski, Bridgman and Flux methods. Numerical modelling will
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North Carolina A&T State University | Greensboro, North Carolina | United States | about 7 hours ago
problems involving big data, extensive computations, and complex modeling, simulation, optimization and visualization. The M.S. in Data Science and Engineering is an interdisciplinary graduate program
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Physics, Chemistry, or a related field. Experience with catalyst simulations. Experience with XAS/EXAFS spectroscopy modeling and its relation to electronic structure theory. Experience with codes like VASP
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energy and mass balance model of Vatnajökull to simulate MP-induced albedo reduction under current and projected climate conditions. The student will be based at Reykjavik University and will undertake
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, visit https://www.biocean5d.org/ . The applicants will work on close collaboration with Jose M. Montoya, from CNRS. - Developing analytical theory and/or simulation models on the relative importance
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs