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, benchmarking and deploy them for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning
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closed-loop materials discovery systems. Familiarity with lipid nanoparticle or critical-materials extraction, separation science, or related chemical-processing applications. Job Family Postdoctoral Job
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of aqueous processing approaches and dry head-end separations methods; evaluating process performance; and contributing to technical reports, publications, and sponsor deliverables. The successful
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properties and behavior of MEMS structures Hands-on experience with signal processing and RF test instrumentation Strong written and verbal communication skills, with the ability to work effectively in
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applications Solid-liquid phase transitions and energetics Mineral processing and separations In situ characterization and apparatus development Work Location This position requires an on-site presence five
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branches of physical sciences, or in math, computer science, and electric engineering who have an interest in accelerator physics will also be considered. Strong programming skills. Proficiency in the
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0-5 years in computer science, mechanical engineering, artificial intelligence, or related engineering disciplines Experience with state of the art AI methods for robotics Experience with robotics
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is extended yearly. Position Requirements A recent PhD (within 5 years) in computational chemistry, chemistry, materials science, physics, computational science, computer science, engineering, or a
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with computational scientists in other divisions, including Mathematics and Computer Science (MCS) and the Argonne Leadership Computing Facility (ALCF). The group has significant involvement in three
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, Microsoft Excel and other computer programming skills. Knowledge of process modeling and life cycle analysis. Knowledge of production processes for critical materials and minerals such as lithium, cobalt