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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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venues Position Requirements Required skills and qualifications: A PhD degree completed within the last 0-5 years (or soon to be completed) in numerical analysis, applied mathematics, computational science
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undergraduates. Postdocs benefit from strong interactions with experts in applied mathematics, computer science, device physics, materials science, and statistics, as well as access to world-leading supercomputing
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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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. The division aims to build lab-wide cross-cutting simulation application capabilities integrating with mathematics, computer science, domain science, and advanced computing architectures and facilities
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field Strong foundation in electrochemistry, electrochemical engineering, and chemical processing Demonstrated experience in mathematical modeling of electrochemical systems; knowledge of solid mechanics
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and contributing to reusable research software when appropriate. Position Requirements Required skills, experience and qualifications: PhD in computer science, applied mathematics, electrical
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approaches. Position Requirements Recent or soon-to-be-completed (typically completed within the last 0-5 years) Ph.D. in mechanical/aerospace engineering, applied mathematics, chemical engineering, or a
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Mathematics, or a closely related field. Design and optimize multimodal LLMs to encode, fuse, and reason over heterogeneous scientific data from diverse modalities such as numerical tables, text, and images
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with