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projects in artificial intelligence, materials engineering, chemistry, and beyond at Argonne National Laboratory. Position Requirements Recently completed PhD within the last 0-5 years in computer science
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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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candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning-based models for large dynamical systems (e.g. weather
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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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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data
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”, “Firstname_Lastname_cover_letter”. Include links to code examples in your CV (e.g., GitHub page, past project repositories). Position Requirements A recent PhD (completed within 5 years, or soon to be completed) in computer science
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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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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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The position is part of a new collaboration between Argonne National Laboratory, the University of Notre Dame, and UIUC, supported by the Quantum Information Science Enabled Discovery 2.0 (QuantISED
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Laboratory’s Biosciences Division, allowing for seamless computational and experimental research integration Position Requirements A recent or soon to be completed PhD within the last 0-5 years Computational