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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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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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platform for X-ray absorption spectroscopy by integrating LLMs, scientific machine learning, physics-aware workflows, and strong computational chemistry/electronic-structure expertise. The researcher will
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and engineers across Argonne, including the Materials Engineering Research Facilities (MERF) and the Argonne MXene Innovations (AMI) program, while collaborating with industrial and academic partners
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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