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Profile PhD in Electrical Engineering, Mechanical Engineering or a related field Background in numerical methods, mathematical modeling, and network simulation or analysis Good understanding of power
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Description Mission: Manage and execute tasks related to morphodynamic modeling and data analysis. Functions to be developed: Design and execute advanced numerical simulations with the Q2Dmorfo model. Manage
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line focused on Negative Triangularity Leadership role: control and data acquisition systems for the SMART tokamak Develop and apply numerical simulations, with emphasis on: o Plasma equilibrium o
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. Plant science/ecology, especially related to forest ecosystems. Computer programming. Data analysis (machine learning, statistics, numerical analysis, time-series analysis, etc.). Quantitative methods in
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive
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. South Carolina Retirement Yes State Insurance Programs Yes Annual Leave Yes Sick Leave Yes Position Description Advertised Job Summary Conduct advanced research in the modeling, simulation, and analysis
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radiative impact assessment; • Validate the methodology using numerical simulations and satellite observations; • Apply the new retrieval method to investigate the geophysical and climatic impacts of sulfate
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, to have the ability to develop and apply new concepts, to write numerical codes and run the simulations, as well as to write clearly and concisely for publication. Required qualifications: • a PhD in
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 2 months ago
limited to: • Numerical modeling efforts, including simulations of long-term production tests, • Laboratory experimental tests on geomechanical stress-strain measurements, • Water conductivity and relative
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. Possible research topics include: Scientific machine learning Numerical partial differential equations (PDEs) Computational fluid dynamics Neural operators High-performance computing Data-driven