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mathematics, or physics to apply their expertise to challenging problems in computational imaging, while collaborating with leading experts in physics, biology, and environmental science. Research Context Soil
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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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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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(within the last 0-5 years) in field experimental physics, engineering, or a closely related field Excellent written and verbal communication skills Demonstrated ability to work effectively in a
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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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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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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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existing efforts in the group and the division. The Argonne High Energy Physics Division provides a vibrant and collaborative research environment. In addition to a strong theory program, the Division has