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Hands-on experience with two-dimensional materials modeling Proficiency in database development and management for computational materials data Strong programming skills and experience with software
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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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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samples and characterize dynamic behaviors. The candidate will be part of a highly collaborative team and actively interact with other groups, including optics, computation, and time-resolved research
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The Multiphysics Computations Section at Argonne National Laboratory is seeking to hire a postdoctoral appointee for performing high-fidelity scale-resolving computational fluid dynamics (CFD
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contribute to open-source code repositories and documentation. Position Requirements Required skills, knowledge and qualifications: PhD in physical oceanography, coastal engineering, computational science
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at technical conferences. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, computer
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computational scientists to advance a next-generation, user-friendly, agentic AI platform for automated data analysis, interpretation, and user interactions. The appointment is expected to last two years and the
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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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chemistry, chemical engineering, physics, computational science, materials science, or related field. Background in synchrotron characterization techniques. Experience collecting and analyzing large