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date); Good knowledge of analytical methods and/or numerical simulations; Very good command of English, both written and spoken. Essential skills and abilities Experience in machine learning; A strong
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
growth, skill development and lifelong learning and enjoy exclusive perks for numerous retail, restaurant and performing arts discounts, savings on local child care centers and special rates on select
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the following domains : - Applied physics (insulating materials, thermal transfer, fluid mechanics, modeling...) - Instrumentation, basic electronics - Notions on simulation using numerical methods Additional
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research experience in computational biology, mathematical modeling, or immunology. Familiarity with numerical methods, parameter estimation, and data visualization. Some knowledge of probability and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
code development or open-source contributions, polymer chemistry, and mathematics, modeling, or numerical methods. An established record of research productivity is highly desired. Special Physical
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 24 days ago
of numerical simulations and digital twins. By using advanced machine learning methods, such as Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (vPINNs), the project
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scientific and numerical skills, meticulous attention to detail, and accuracy. Spanish language proficiency is preferred. Workplace Requirements This position is offered as a hybrid role, combining both in
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to the design, implementation, and scientific exploitation of numerous satellite missions. CESBIO is a Unité Mixte de Recherche (Joint Research Unit) bringing together the CNES, CNRS, IRD, and the University
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density functional to ab initio methods coupled to effective field theory) and related numerical and formal techniques. We are particularly fond of interdisciplinary connections and emerging technologies
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numerical models, kept on track with satellite data using ensemble data assimilation methods. But ocean forecasting research is also adopting ML gradually, slower due to the lack of ocean observations