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Field
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, optimization and the modeling of critical infrastructure (gas pipelines and electrical grids). These application areas are tied together by research in novel algorithms, numerical methods and machine learning
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, numerical optimization, numerical partial differential equations, and parallel computing. The Researcher will join a project developing parallel high-order meshing algorithms from medical images and parallel
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 11 minutes ago
PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 25 days ago
PhD in a relevant field such as structural mechanics, heat transfer, numerical optimization, topology optimization, or lattice design. The postdoctoral scholar will be responsible for vigorously
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will focus on studying the impact of pharmacist involvement in self-measured of blood pressure. This position will offer numerous opportunities for the right candidate to hone their experimental, project
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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scientist and engineers at the University of Cincinnati as well as Procter & Gamble across numerous scientific domains. Utilize mathematical modeling, statistical techniques, chemistry, physics, and
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in adaptive immune systems (e.g., co-evolution of bacteria and phages, as well as T and B cells with pathogens). • Physics-informed machine learning of biophysical systems (e.g., developing optimal
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algorithms and codes for AI-enabled digital twin technologies. Design advanced numerical algorithms for partial differential equations and optimization problems related to digital twin technology. Implement
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requires not only expertise in LLMs and machine learning but also an understanding of the unique challenges posed by scientific data, which often includes large-scale numerical datasets, complex simulations