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) simulations and reduced order modeling of turbulent and reacting flows relevant to advanced propulsion and power generation systems, such as gas turbines and detonation engines. The successful candidate’s
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computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced
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spectrometers at the Advanced Photon Source. The successful candidate will work at the interface of cutting-edge cryogenic detector technology and synchrotron science, helping to integrate TES spectrometers
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. Education: Ph.D. (< 5 yrs. since Ph.D.) • Familiarity with image processing and simulation software. • (Preferred) Experience with nanofabrication, transport measurements, thin film deposition, in-situ TEM
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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 preparation of proposals and participating in sponsor interactions. Applies the models to selected case studies. Document and publish to our internal software repository. Supports multidisciplinary teams in
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Argonne is a multidisciplinary science and engineering research center, where talented scientists, engineers, and business professionals work together to answer the biggest questions facing humanity
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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Laboratory. This is an opportunity to be part of a team helping with the design of new system software and runtime infrastructure to improve the performance and energy efficiency of future scientific computing
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scholarly work or industry experience in economic and supply chain analysis, computational modeling, or policy analysis. Excellent oral and written communication skills in scientific and engineering contexts