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-informed AI framework that decodes the complex relationships between material defects, functional fields (e.g., strain, electrostatic potential), and device performance, with a primary focus on leveraging
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, ptychography, Laue microdiffraction, or related coherent/imaging techniques. Proven ability to design, conduct, and analyze complex synchrotron experiments. Proficiency in scientific programming (Python, MATLAB
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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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complex instruments and run simulations to accelerate discovery. This involves navigating vast parameter spaces, identifying rare or transient phenomena, and dramatically optimizing the use of precious
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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 Chemical Sciences and Engineering Division is seeking applicants for a postdoctoral appointee who will conduct computational research in Selective Interface Reactions (e.g., atomic layer
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facilities in partnership with the computational science community. We help researchers solve some of the world’s largest and most complex problems with our unique combination of supercomputing resources and