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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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optimization schemes. From developing AI models to uncover structure-function relationships with limited data sets, to building automated electrode-electrolyte interface discovery workflows and implementing full
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planning and operations, and the interaction with other infrastructures. Some of these tools will require the development of underlying optimization methodologies and analytics frameworks. The successful
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complex instruments and run simulations to accelerate discovery. This involves navigating vast parameter spaces, identifying rare or transient phenomena, and dramatically optimizing the use of precious
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capabilities. Key Responsibilities: Capturing requirements and analyzing specifications for future detector systems Performing conceptual design and system-level optimization using emerging computing
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, advanced computational techniques, and data science. The project involves: Materials Development Platform that will enable redox molecule optimization via predictive simulations, database management, and AI
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The Data Science and Learning Division (DSL) at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting edge molecular and microbiology work to enhance non-proliferation
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Argonne National Laboratory is seeking a highly skilled Postdoctoral Researcher to work in the Applied Materials Division (AMD). In this position the successful candidate will conduct applied
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define requirements and performance specifications for future HEP/NP detector systems Perform detector concept development, system-level design, and optimization leveraging emerging computing architectures
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optimization technologies are revolutionizing the way power grid is operated and planned. CEEESA is seeking talented and motivated researchers to enhance its capability in solving energy challenges using