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for critical energy and technology sectors. Ability to assess the economic and operational impacts of large-scale AI adoption (e.g., data centers, compute infrastructure) on U.S. electricity demand, generation
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Extraction), jointly led by the Chemical Sciences and Engineering (CSE) and Applied Materials (AMD) Divisions at Argonne National Laboratory. This project focuses on understanding the evolution of structure
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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Recent or soon-to-be completed (typically within the last 0-5 years ) Ph.D. in Computer Science, Electrical Engineering, or a related field. Demonstrated research expertise in AI and machine learning, with
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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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) in the field of accelerator physics or a closely related science and engineering discipline Strong experience developing and applying computational modeling and simulation Familiarity with accelerator
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physics (HEP) and nuclear physics (NP) experiments. The successful candidate will be a key member of a multidisciplinary co-design team integrating materials science, computing, and device engineering to
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, networking, and leadership. Position Requirements This level of knowledge is typically achieved through a formal education in economics, operations research, public policy, environmental science, engineering
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-based experiments, including accelerating gradient tests and breakdown studies of accelerating components. Collaborate with the APS/ASD mechanical engineering group on the fabrication of accelerator
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, you will: Apply engineering principles to develop molten salt synthesis and separations processes to support fuel cycle science and technology. Develop and test new electrodes for use in molten salt