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: Conduct research and analysis and develop models to aid decision-making around topics related to improving the domestic and global resilience of supply chains for technologies vital to clean energy and
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develop correlations to describe the characteristics of materials and models to describe their behavior in typical reactor environments. You will participate in planning, developing, and implementing
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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The Applied Materials Division at Argonne National Laboratory has an immediate opening for a Postdoctoral Appointee. The candidate will develop high performance material models and finite element
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to catalyst/electrolyzer system design, optimization, integrated process modeling, techno-economic analysis, and environmental impact assessments. Additionally, the candidate will prepare reports, presentations
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) for applications in High-Energy Physics (HEP). We seek highly qualified candidates with experience in ML algorithms including unsupervised techniques. The candidate is expected to lead an effort to prepare
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(HPC). In addition, the prospective postdoctoral appointee will develop reduced-complexity models for accurate prediction of fluid dynamical systems for the purposes of increasing computational
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The X-ray Science Division (XSD) at Argonne National Laboratory invites applications for a postdoctoral researcher position to develop novel X-ray diffraction techniques for 3-dimensional imaging
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The Low-Energy Nuclear Physics Research Group (LER) of the Physics Division at Argonne National Laboratory seeks outstanding individuals to fill an open postdoctoral position to develop and conduct
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energy systems, develop new models and datasets, and provide rigorous and objective results. We are currently seeking applicants with life cycle analysis experience in one or more of the following areas