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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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target properties. The candidate will help draft and publish technical reports, conference papers, and journal publications describing the research outcome and will be expected to attend and present
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computational scientists to advance a next-generation, user-friendly, agentic AI platform for automated data analysis, interpretation, and user interactions. The appointment is expected to last two years and the
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simulations, design and conduct experiments, and analyze multimodal data streams in a continuous, real-time loop with minimal human intervention (https://www.nature.com/articles/s41524-024-01423-2 , https
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computer-aided design software. Collaborative skills, including the ability to work well with other divisions, laboratories, and universities. Ability to demonstrate strong written and oral
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computational materials science aligned with CNM strategic themes and the DOE mission Publish in refereed journals and present at conferences, symposia, and seminars Contribute to proposal development and assist
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operando characterization using the Advanced Photon Source (APS). This work will support critical material recovery and battery recycling initiatives. The successful candidate will help drive next-generation
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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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-driven modeling (including ML/AI where appropriate) to help anticipate vulnerabilities and inform decision-making for energy deployment and national competitiveness. In this role you will : Conduct and
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The Energy Systems and Infrastructure Assessment (ESIA) division provides the rationale for decision makers to improve energy efficiency. We develop and use analytic tools to help the U.S. achieve