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and technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will conduct comprehensive supply chain mapping, modeling, and analysis—integrating
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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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rules. Ability to work with large volumes of hazardous chemicals. Flexibility to change projects and work on a variety of projects simultaneously Ability to model Argonne’s core values of impact, safety
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or a related field. Extensive experimental expertise is essential. Additionally, expertise in the modeling of electrochemical processes is highly desirable. 0-3 years past Ph.D. work experience is
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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materials, recycling, mineral processing, and separations. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. This position requires an on-site presence at the Argonne
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to detector/system modeling and optimization for count rate, resolution, and throughput. Document methods and develop user-facing procedures and best practices for reliable operation during user runs
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agencies and other national laboratories. The candidate will develop power systems and electricity market modeling, and analytics tools that support energy, economic, and financial analyses of power grid
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, instrumentation, modeling, and data science Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field(s) of materials science, physics, computational science, or a related field