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Biology: Strong background in systems biology and regulatory network modeling Interdisciplinary Collaboration: Experience working across disciplines with computational biologists, computer scientists, and
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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scientists with extensive microelectronics (materials and devices), AI, computational materials science and materials characterization expertise; and will be expected to bring the electrochemical expertise
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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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computational scientists to advance a next-generation, user-friendly, agentic AI platform for automated data analysis, interpretation, and user interactions. The appointment is expected to last two years and the
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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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multidisciplinary team of scientists and High Performance Computing (HPC) engineers. In the AL/ML group, we work at the forefront of HPC to push scientific boundaries, carrying out research and development in state
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to microelectronics. The candidate will be part of a highly interdisciplinary project involving X-ray scientists, physicists, materials scientists, and computational scientists to solve challenging problems in
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research will involve synergetic collaborations with a multi-disciplinary team involving engine modelers, CFD experts, and computational scientists to enhance the predictive capability for next-generation
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Argonne’s Nanoscience and Technology Division seeks a postdoctoral scientist to advance transmission electron microscopy (TEM) studies of materials and interfaces relevant to microelectronics