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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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-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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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
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instrument proposed under a DOE Major Item of Equipment (MIE) effort. Building on two decades of APS XRS capability (including the LERIX program at 20-ID) and recent commissioning work at Sector 25
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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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an independently-funded research program within 2-3 years. The successful candidate will develop and apply advanced data-driven methodologies to accelerate discovery in materials/chemistry design, characterization
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technologies, control algorithms and powertrain architectures with focus on advanced technologies. The candidate will assist on projects to benchmark next generation vehicle systems, identify opportunities
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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 Computational Science Division (CPS) at Argonne National Laboratory (near Chicago, USA) is seeking a postdoctoral researcher to enable exascale atomistic simulations of ferroelectric devices
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technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will assist with data collection, analysis, and scenario modeling for a DOE-sponsored assessment