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math, HPC, signal processing, computational physics and materials science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of
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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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measurements. Postdocs have an initial term of 1 year and can be renewed in 1 year increments; up to a total of 3 years depending on funding and performance. The expected starting date is Q3/Q4 of 2025
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks a highly motivated postdoctoral researcher to join a multidisciplinary team advancing quantum information
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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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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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-throughput workflows for data acquisition and analysis Contribute to on-the-fly data processing and integration with computational tools Collaborate with multidisciplinary teams in nanofabrication
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and