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Programming experience in scientific computing environments Preferred Qualifications: Experience developing Finite Element Method or CFD models for composite manufacturing applications Knowledge of machine
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced
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management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL
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a unique opportunity to develop cutting-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency
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Sciences Directorate, at Oak Ridge National Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) and machine learning/artificial
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optical systems, thermal imaging, pyrometry, spectroscopy, high speed imaging or acoustic sensing. Familiarity with data analytics, machine learning, or signal processing. Knowledge of metal additive
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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
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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Program (DOE IP) to advance the chemical processing of unique f-element isotopes, including Cf-252, Bk-249, Es-254, Fm-257, and Pm-147. A core focus of the DOE IP is to improve and develop novel chemical