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skills Demonstrated ability to work effectively in a collaborative, interdisciplinary research environment Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork
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operando experiments under electrical, thermal, or mechanical bias to capture real-time defect dynamics. Integrate multimodal datasets and collaborate with AI/ML teams for data fusion, physics-informed model
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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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computational science expertise. The Computational Science (CPS) Division focuses on solving the most challenging scientific problems through advanced modeling and simulation on the most capable computers
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-on experience in the fabrication and testing of all-solid-state cells, including coin-cell and/or model cell configurations Knowledge of synchrotron-based X-ray characterization techniques, such as X-ray
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sensing, quantum information science, superconducting circuit, or magnonics. Proficiency in scientific software development (e.g., Python, COMSOL, HFSS or similar languages). Ability to model Argonne’s core
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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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information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum
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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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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