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. The project will involve development of novel parallel algorithms to facilitate in-situ analyses at-scale for multi-million and multi-billion atom simulations. In this role, you can expect to work on enhancing
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geometry manipulation with computer-aided design software. Experience with coupling CFD and FEA codes. Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and
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. Knowledge of multi-dimensional code development (in C++/C/Fortran) for two-phase/multiphase flow and turbulent combustion applications, and parallel scientific computing. Experience in geometry manipulation
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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processes in internal combustion engines (ICEs), such as fuel injection, combustion, heat transfer, etc. Improve, develop, and implement CFD sub-models necessary to enable predictive ICE simulations
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Computer Science, Computational Science, Applied Mathematics, or a related field. Experience in scientific visualization, computer graphics, or advanced rendering. Strong background in HPC, parallel computing, and
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chemical or mechanical engineering, or an associated field. Knowledge of chemical and thermal processes. Knowledge of electrochemical systems. Knowledge of water electrolysis including materials, durability
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and energy conversion systems. Knowledge of computational techniques and numerical methods. Knowledge of computer simulation and data analysis. Knowledge of C/C++ language and parallel programming with
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relevant numerical methods to dramatically reduce time to a feasible solution, parallelization of computations/high-performance computing, and other emerging and novel techniques to improve the efficiency
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is preferred. Research experience in one or more of the following areas: 1.) Complex systems modeling, including simulation or analytical modeling, 2.) High performance computing, parallel programming