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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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communication skills are required Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork Applicants should prepare a CV, research statement, and arrange for 3 letters
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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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multi-physics simulation codes. Develop accurate and computationally efficient CFD models and perform simulations of the entire chain of physics and chemistry involved with fuel-air mixing, turbulent
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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