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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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and carbonation reactions in aqueous solutions, amorphous materials and at mineral-water interfaces. Collaborate with ORNL researchers on the design and execution of experiments to measure
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applications. You’ll help design, train, and evaluate AI systems that plan, reason, and take actions to accelerate scientific discovery across domains (materials, chemistry, climate, fusion, biology, and more
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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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, beam transfer lines and SNS Ring. The qualified candidate will also design software to monitor and control the SNS accelerator in real time and work in SNS Control Room. As a U.S. Department of Energy
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
familiarity with AI/ML algorithms, for generative materials design, or for knowledge extraction, e.g. causal ML or symbolic regression, etc. Strong demonstrated background in coding for data analysis using
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uncertainty quantification. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and
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://www.ornl.gov/directorate/isotopes for more information about ISED. This position resides within the Target Design, Analysis, and Qualification (TDAQ) group in the Radioisotope Production Engineering and Analysis