30 postdoctoral-machine-learning Postdoctoral positions at Oak Ridge National Laboratory
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topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work
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), machine learning and Artificial Intelligence to enhance our capabilities in making AI-ready scientific data. As a postdoctoral fellow at ORNL, you will collaborate with a dynamic team of scientists and
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Expertise in machine learning and big data analysis Excellent written and oral communication skills Motivated self-starter with the ability to work independently and to participate creatively in collaborative
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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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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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, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making
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machine learning tools for detection, diagnosis, and correction of sensor faults Report results in peer-reviewed publications Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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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