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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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. 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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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
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optoelectronics. The successful candidate will build and lead an innovative research program in microelectronics by leveraging CNM’s unique strengths in materials synthesis, device fabrication, and characterization
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and engineers across Argonne, including the Materials Engineering Research Facilities (MERF) and the Argonne MXene Innovations (AMI) program, while collaborating with industrial and academic partners
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with internal and external research partners to advance project objectives and meet program milestones Analyze experimental data and prepare reports, technical presentations, and updates for internal