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manuscript to be submitted). Position Requirements This level of knowledge is typically achieved through a formal education in electrical engineering, mechanical engineering, physics, or a related field at the
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The Chemical Sciences and Engineering Division is seeking applicants for a postdoctoral appointee who will conduct computational research in Selective Interface Reactions (e.g., atomic layer
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will support this mission by building and applying modular, physics-informed frameworks to explore design tradeoffs, evaluate novel process concepts, and inform early-stage technology development and
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dynamics (CFD) to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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Leadership Computing Facility (ALCF), the Mathematics and Computer Science Division (MCS), the Computational Science Division (CPS), and the Data Science and Learning Division (DSL). The postdoctoral
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The Chemical Sciences and Engineering (CSE) Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge research in the development of advanced electrolytes
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preferred. Strong data analysis skills and ability in programming languages, such as Python, for performing experiments and analyzing data. Knowledge of innovative phase retrieval algorithms is desirable
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The Nuclear Technologies and National Security Directorate (NTNS) is seeking a highly qualified and motivated Postdoctoral Researcher specializing in energy economics. This role is pivotal in
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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