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. The candidate will work closely with computational modeling collaborators to validate reactor designs and optimize operating parameters. The candidate will be expected to contribute to report preparation
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. The selected candidate will develop computational models at the mesoscale and/or macroscale based on the principles of mass, momentum, and energy conservation to describe processes such as morphological change
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, physics, computer science, and/or data science Demonstrated accomplishments in materials informatics, scientific machine learning, or AI-guided experimental design Strong Python and scientific computing
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extended yearly. Position Requirements A recent PhD (within 5 years) in computational chemistry, chemistry, materials science, physics, computational science, computer science, engineering, or a related
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may include work at Jefferson Lab, the Electron-Ion Collider (EIC) program, detector research and development, and applications of AI in nuclear physics. Applications received by Tuesday, November 4
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from various scientific backgrounds (Physics, Chemistry, Materials Science, Geoscience, and Engineering, etc.) will be considered. Recent or soon-to-be completed PhD (within the last 0-5 years) in
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four staff members [Ian Cloët, Alessandro Lovato, Anna McCoy, and Yong Zhao] and several postdocs and students. The group has a broad research program in QCD/hadron physics and nuclear structure
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in experimental physics and superconducting device development, with a focus on advancing multipixel single-photon camera technology and multiplexed readout for quantum information science applications
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liquid and solid-state electrolytes using artificial intelligence, under the guidance of a supervisor. The successful candidate will contribute to projects involving the synthesis, physical and chemical
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-completed PhD with strong background in Materials Science or Physics (within the last 5 years) Considerable experience in understanding magnetic-domain physics in thin film and/or nanostructured materials