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among material properties, electrochemical performance, and battery system cost at the material, cell, and pack levels. The researcher will plan and advance performance and cost modeling of energy storage
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of radiofrequency (MHz–GHz) nanoscale phenomena in systems relevant to microelectronics and quantum information science. Opportunities also exist for cross-platform studies integrating ultrafast TEM with ultrafast x
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hardware. Duration of the appointment is one year initially and renewable for up to three years contingent on performance and funding. The successful candidate will be supported by ALCF's Performance
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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance
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clustering, redshift-space distortions, weak/strong gravitational lensing, and artificial intelligence/machine learning (AI/ML). The observational focus is on optical sky surveys (DES, DESI, Roman, Rubin Obs
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Coherent Diffraction Imaging (BCDI), ptychography, and X-ray Photon Correlation Spectroscopy (XPCS). The goal is to move beyond simple correlations to discover the causal, governing rules of defect-property
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MEP Group Argonne National Laboratory, situated near Chicago, is a prominent multidisciplinary science and engineering research center. The Medium Energy Group in the Physics Division, comprises eight
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modeling of x-ray spectroscopies sensitive to molecular chirality; simulations of x-ray–induced ultrafast electron-transfer, decay, and nuclear dynamics in gas- and liquid-phase systems; and the development
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facilities. Collaboration with researchers at Fermilab, the University of Chicago, and Northwestern University is strongly encouraged. Applicants should submit Curriculum vitae Brief statement of research
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The Materials Science Division is seeking applicants for a Postdoctoral Appointee who will conduct cutting-edge research in AI for Materials Chemistry, with a focus on energy storage and conversion