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collaborating with a software engineering team to translate research into production-ready tools. The successful candidate will be part of an inter-lab, highly inter-disciplinary team of experts in ML, applied
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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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expertise combining AI and computational chemistry software in HPC platforms for materials science discovery research. Effective written and oral communication skills. Ability to model Argonne's core values
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Hands-on experience with two-dimensional materials modeling Proficiency in database development and management for computational materials data Strong programming skills and experience with software
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Extensive knowledge of Microsoft Excel and good computer programming skills Knowledge of techno-economic analysis and life cycle analysis Experience working with Argonne’s EverBatt model, GREET model, and
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the domains of environmental, water, and energy system analysis. Prepares reports, papers, and presentations for conferences, workshops, and technical journals. Supports program development including