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advanced computing, optimization, and data analytics technologies. The postdoctoral researcher will work with a team of researchers on solving challenging problems using optimization, stochastic models
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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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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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developing machine learning surrogates and emulators for dynamical systems. Proficiency in managing large datasets and training with GPU-enabled computing resources. Expertise in numerical optimization and
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in data management and high-performance computing, including workflow design and optimization Strong oral and written communication skills, with the ability to work effectively with internal and
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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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terms of universal computational skills. We encourage you to describe your work using broader technical terms—such as "statistical data analysis," "workflow automation," or "algorithm optimization"—rather
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multidisciplinary team, the candidate will work at the intersection of AI/ML, domain sciences, and high-performance computing. The role requires a strong foundation in LLMs and machine learning, along with
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-directed, collaborative research aligned with CNM’s strategic plan and for user support, including enabling workflows that couple computation, AI, and experimental measurement. Expertise in one or more of
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(CFD) to develop and optimize new processes and equipment designs using high-performance computing Develop process- and facility-scale models as the foundation for digital twins of chemical processing