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other institutions is essential to develop a full understanding of the complex chemical systems. Position Requirements This level of knowledge is typically achieved through a formal education at the Ph.D
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current research gaps and prioritize analysis of ongoing and postulated events. The position will also work to develop training materials and related training aids/key leader engagement tools to aid in
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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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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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for completing complex tasks, such as designing, parameterizing, and assessing simulations. The candidate will be expected to research and develop novel approaches for LLM-based agentic code generation and
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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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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