56 evolution "https:" "https:" "https:" "https:" "https:" "Swedish University of Agricultural Science" Postdoctoral positions at Argonne
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have a strong background in fundamental electrochemistry, with preferable hands-on expertise in computational materials science. The applicant should be well versed in code development, application of AI
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-generation nuclear science experiments at Jefferson Lab and the Electron-Ion Collider (EIC). As part of our growing multidisciplinary team, you will contribute to the development of superconducting nanowire
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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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We are seeking a highly motivated Postdoctoral Appointee with a strong background in artificial intelligence and machine learning (AI/ML), with particular emphasis on the development and application
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photonic platforms through nano- and meso-scale lithographic fabrication. This position supports two complementary, three-year Laboratory Directed Research and Development (LDRD) projects focused on hybrid
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systems. Development, evaluation, and applying machine learning/computational approaches, synthesis activities, computational tools, compiling results, preparing reports, publications, and documentation
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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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; present findings internally and at external conferences; contribute to publications Collaboration Environment Work in close coordination with ML development teams across Argonne (including the Advanced
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Roman Space Telescope in the Cosmic Frontier. The Division is also engaged in physics preparations for the Electron-Ion Collider (EIC), the development of advanced accelerator technologies (such as the
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We are seeking a highly motivated and adaptable postdoctoral researcher to join the Laboratory Directed Research and Development (LDRD) project FREEZE (Fundamental Research on Energy-Efficient Zonal