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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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the Scaling Machine Learning (SML) effort of the High-Energy Physics Center for Computational Excellence (HEP-CCE), the candidate will be responsible for facilitating the scaling of HEP ML workflows
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energy physics, physics, and computational science Programming expertise in C/C++, Python, Fortran, or another scientific programming language Comprehensive knowledge of Input/Output and data persistence
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. Knowledge of C/C++ language and parallel programming with MPI. Background in modeling of engineering systems. Familiarity with high performance computing (HPC) software platforms is a plus. Skills in using
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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