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quantum transduction and terahertz (THz) photon generation via enhanced light–matter interactions. The postdoc will lead efforts in device patterning and the integration of complex materials—such as
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The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in scientific machine learning, focusing
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chemistry, chemical engineering, physics, computational science, materials science, or related field. Background in synchrotron characterization techniques. Experience collecting and analyzing large
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on developing a new hybrid light–matter platform that couples transition metal complexes with optical microcavities to achieve optical control over ultrafast spin conversion and charge transfer
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integration Optimization and stochastic modeling methodologies Energy storage Electricity market analysis Supports multidisciplinary teams in the application of these methods and tools to complex issues in
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to microelectronics. The candidate will be part of a highly interdisciplinary project involving X-ray scientists, physicists, materials scientists, and computational scientists to solve challenging problems in
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. Demonstrated ability to design and execute experiments, analyze complex datasets, and communicate results effectively through publications and presentations. Strong teamwork, communication, and organizational
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in multiphase systems Use modeling tools such as computational fluid
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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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-driven initiatives within computational science Effective communication skills, both verbal and written, for effective collaboration with interdisciplinary teams and clear presentation of complex technical