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collaboration and innovation. Position Requirements This level of knowledge is typically achieved through a formal education in Statistics, Machine Learning, Computer Science, Logistics/Supply Chain, or a related
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-the-loop exploration of extreme-scale scientific data. This position sits at the intersection of scientific visualization, agentic AI systems, human–computer interaction (HCI), and high-performance computing
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bench scale micro-computed tomography and ultrasonic sensing methods to evaluate the state of charge and state of health of iron- and lead-based electrodes. Your research will be complemented by studies
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microwave and/or optical device measurement and characterization Knowledge of quantum information science Design, fabrication, and measurement of superconducting qubits Research involving microwave-optical
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-completed Ph.D. within the last 0-5 years in Atmospheric Science, Meteorology, Climate Science, Applied Mathematics, Data Science, or a related field with strong quantitative and computational research
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is supported by a DOE-funded research program on ultrafast science involving Argonne National Laboratory, University of Washington, and MIT. The goal of this research program is to understand and
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an independently-funded research program within 2-3 years. The successful candidate will develop and apply advanced data-driven methodologies to accelerate discovery in materials/chemistry design, characterization
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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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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symmetries, and nuclear data. LER also plays a critical role for the ATLAS National User Facility, where it provides support for ATLAS Users, conducts its own research program, and develops and operates