43 machine-learning-postdoc-"https:" Postdoctoral positions at Oak Ridge National Laboratory
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Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods to advance multi-scale coupled physics simulations in support of the missions and programs of the US
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properties of the above materials. Collaborate with ORNL postdocs and staff who are involved in structural characterization. Participate in the development of new ideas and projects. Present and report
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, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The successful candidate will design and implement differential privacy
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such as federated learning. Provenance and Reproducibility Frameworks: Build systems that enable detailed provenance tracking, schema validation, and auditable workflows to ensure trustworthy 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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of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a
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. Implement and optimize data representations and pipelines suitable for machine learning and uncertainty quantification. Collaborate with AI/ML experts to design and test inference methods that map
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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and
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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