31 high-performance-computing Postdoctoral positions at Oak Ridge National Laboratory in United States
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experience in hydrological or Earth system modeling, with emphasis on process understanding and prediction. Strong background in computational sciences, including numerical methods, high-performance computing
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Preferred Qualifications: Experience with high-level programming environments, such as Python. Excellent written and oral communication skills. Motivated self-starter with the ability to work independently
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-edge high-performance computing (HPC) that incorporate machine learning/artificial intelligence (ML/AI) techniques into visualizations, enhancing the efficiency and reliability of scientific discovery
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Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) and machine learning/artificial intelligence (ML/AI) techniques that incorporate
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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Directorate, at Oak Ridge National Laboratory (ORNL). This position presents a unique opportunity to develop cutting-edge high-performance computing (HPC) that incorporate machine learning/artificial
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computing AI on High-Performance Computing (HPC) cluster. Examples on areas of research interest include but are not limited to: Vision transformers. AI foundation models. Computing and energy-efficient
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the following areas are required: 1) wetland science; 2) hurricane science; 3) remote sensing; 4) deep learning and AI, 5) high-performance computing. Experience using AI models is required; experience
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binding and selectivity manifested in improved separations. Develop and evaluate statistical mechanical and machine learning tools for studying molten salts using the leadership-class high-performance
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safety at ORNL and DOE sites. This position resides in the Performance Engineering group in the Data and AI Systems Section in Computer Science and Mathematics division within Computing and Computational