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PhD (within the last 0-5 years) in field of materials science, chemistry, chemical engineering, computer science, or a related field Experience operating and troubleshooting laboratory automation
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, or network-based modeling of infrastructure or industrial systems. Familiarity with high-performance computing, cloud computing, or parallel computing environments for training models and solving optimization
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bench scale micro-computed tomography and ultrasonic sensing methods to evaluate the state of charge and state of health of iron- and lead-based electrodes. Your research will be complemented by studies
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candidate will be part of a highly collaborative team and actively interact with Argonne and UChicago scientists as well as external collaborators. Position Requirements Recent or soon-to-be-completed PhD
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. In this role, you will lead a research program centered on AI-driven autonomous synthesis, including: Active learning and Bayesian optimization over synthesis parameters such as precursors, temperature
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-be-completed PhD (typically completed within the last 0-5 years) in chemistry, chemical engineering, materials science or related field. Knowledge and hands-on experience in the following areas
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), in collaboration with Argonne researchers and external partners from industry and academia. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in
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. The candidate will work closely with computational modeling collaborators to validate reactor designs and optimize operating parameters. The candidate will be expected to contribute to report preparation
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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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In the Computational Materials Group, we focus on the development and use of computational and theoretical methods to understand and predict the behavior of solids, liquids, and nanostructures from