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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory is seeking postdoctoral researchers to work on distributed quantum computing. The project aims to develop superconducting
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We are seeking a highly motivated Postdoctoral Researcher with expertise in computational biology, deep mutational scanning data, and generative artificial intelligence (AI). The successful
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We are seeking a Postdoctoral Appointee to work in the Mathematics and Computer Science (MCS) Division of the Computing, Environment, and Life Sciences directorate (CELS) of Argonne National
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HEP-CCE (Center for Computational Excellence) Storage Optimization. The HEP Division performs cutting-edge research facilitated through advanced detector development, high-performance supercomputing
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The Chemical Sciences and Engineering (CSE) Division is seeking applicants for a postdoctoral appointee who will conduct computational research in Catalysis Science. The project involves performing
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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-inspired research relevant to microelectronics. The candidate will be part of a highly interdisciplinary project involving X-ray scientists, physicists, materials scientists, and computational scientists
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-house codes and making use of high-performance computing (HPC) tools. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical, aerospace
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The Nuclear Science and Engineering (NSE) Division is seeking a postdoctoral appointee to develop computational methods and computer codes to model the physics and engineering of advanced nuclear
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the Scaling Machine Learning (SML) effort of the High-Energy Physics Center for Computational Excellence (HEP-CCE), the candidate will be responsible for facilitating the scaling of HEP ML workflows