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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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in modeling, analysis and control of electric power distribution and transmission system, applying state of the art machine learning (ML) and deep learning algorithms to develop cybersecurity
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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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objectives and performance targets. Develop or modify electrode materials, membranes, or other components to enhance separation performance. Collaborate with multidisciplinary teams to scale electrochemical
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unit operations performed in molten salt electrolytes. As a part of this team, you will: Perform integrated system modeling and data analyses to develop and apply complex flowsheets and process models
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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