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of the fastest-growing academic health centers in the nation, Texas A&M Health encompasses five colleges and numerous centers and institutes working together to improve health through transformative education
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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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supporting air quality research, policy development and evaluation of air pollution interventions. This role uses numerical models of atmospheric chemistry and transport (GEOS-Chem, and others) to increase
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. The project activities will involve the development of the theory and implementation of the advanced mechanics and numerical models as well as constitutive model calibration and validation based on physical
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the experimental behavior and the numerical modeling of these tests. A series of pushover tests will be carried out, during which the type of earth used, the slenderness, vertical load, and repeatability will be
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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; Couple the model to a 1D physical model (GOTM) through the FABM framework; Validate the coupled model using published and newly gathered observational data; Conduct numerical experiments to understand
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provision of user-tailored climate services including products related to climate modeling. The successful candidate will have experience in two or more of the following areas: numerical climate modeling
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research and high-level teaching have enabled it to establish double degree agreements with prestigious universities and advanced partnerships with numerous companies. With its focus on sobriety, energy, the
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and