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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Knowledge in modeling and algorithms for large-scale ordinary differential equations (ODEs) and differential-algebraic equations (DAEs) Proficiency in a scientific programming language (e.g., C, C++, Fortran
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++, or similar languages. Demonstrated expertise in machine learning, especially in the context of dynamical systems modeled by differential-algebraic equations. Experience with high-performance computing and the
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reconstruction algorithms that incorporate multiply-beam coherent scattering imaging in a grazing incidence geometry to improve the spatial resolution to ultimately demonstrate the utility of the novel coherent
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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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combustion engines. Experience with CONVERGE CFD software. Experience in geometry manipulation with computer-aided design software. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long