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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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Computer Science, Electrical/Computer Engineering, AI/ML, or a closely related field. Demonstrated experience in AI/ML model development, LLM tuning, generative AI, functional safety and risk analysis. Proficiency
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or machine learning potentials (iv) modeling of the solid and aqueous interfaces. Research proposal or concept writing experience. Programming experience for workflow development and scientific computing
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Substantial programming skills using Python or modern C/C++ Experience with machine learning and deep learning libraries Experience building AI models in platforms such as TensorFlow, Keras, or PyTorch
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, machine learning, geographical information sciences, and many other topics to help frame and solve the above problems on a national and global scale. The successful candidate will contribute to cutting-edge
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journals and conferences. This role provides a unique opportunity to work with the world’s first exascale system, Frontier, and collaborate with leading experts in machine learning, optimization, electric
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale