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experimental facilities. You will be responsible for developing energy-efficient, physics-aware algorithms designed for distributed learning across both high-performance and edge computing environments. You will
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discovery. This position is for the Discrete Algorithms group, Mathematics in Computation Section, within the Computing and Computational Sciences Directorate at Oak Ridge National Laboratory (ORNL) and
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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
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within a multi-disciplinary research environment consisting of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing
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across diverse clients. You will use Frontier's computational power to scale and validate these privacy-preserving algorithms, enabling breakthroughs across energy and image modeling domains. You will also
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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, high-performance computing (HPC), and computational sciences. Major Duties/Responsibilities: Participate in: (1) design and implementation of scalable DL algorithms for atomistic materials modeling
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, demand-flexible, and affordable buildings for the DOE Building Technologies Office (BTO), the Federal Energy Management Program (FEMP), and Office of State and Community Energy Program (SCEP). Major Duties
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prediction and assessment. Design algorithms integrating geophysical principles with explainable and interpretable AI techniques to enhance model interpretability and uncertainty quantification. Contribute
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computed tomography (CT) reconstruction, including sparse-view and limited-angle algorithms, and the application of advanced machine learning (ML) and computational imaging methods to scientific and