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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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PhD level with zero to five years of employment experience. Expertise in testing, characterizing, and measuring MEMS devices and designing feedback loops and control algorithms for the precise operation
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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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We invite applications for a Postdoctoral Appointee to contribute to a growing research program in process systems modeling and optimization for clean energy, critical materials, and advanced
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phenomena Create new reduced-order models and submodels related to fluid flow, heat transfer, thermochemistry, and electrochemistry in multiphase systems Use modeling tools such as computational fluid
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The Applied Materials Division (AMD) at Argonne National Laboratory is seeking a Postdoctoral Appointee to contribute to the research, development, and manufacturing of advanced cathode materials for next-generation lithium- and sodium- ion batteries. This role provides a unique opportunity to...
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experiment research program, particularly in the ATLAS experiment. The successful candidate is expected to take a leading role in data analysis, detector construction, and experiment operations. In
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Intelligence, Machine Learning, Quantum Information and Quantum Simulation. The successful candidate will be expected to lead an independent research program in particle theory to strengthen and complement
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modeling tools to develop and optimize new processes and equipment designs using high-performance computing Analyze data, prepare manuscripts for submission to peer-reviewed publications, prepare technical
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The Advanced Photon Source (APS) at Argonne National Laboratory invites applications for a postdoctoral position focused on developing novel computational approaches for multi-modal biomedical image