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knowledge gaps in high-pressure science and publish the results in scientific journals. The initial appointment is for two years and may be extended to a third year, contingent on performance, project
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materials from complex feedstocks to achieve the desired product quality and form. As a part of this team, you will: Apply electrochemical engineering principles to develop processes such as oxide reduction
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or equivalent. Knowledge and experience with analytical techniques such as XRD and SEM. Skill in devising and performing experiments to acquire data, using and maintaining research equipment, compiling
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(CO2) conversion processes and contribute to engineering design of upscaled processes. The candidate will be a part of the Applied Materials Division (AMD) within AET at Argonne and will contribute
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/reactions, with increasing emphasis on using artificial intelligence and quantum information science. The group has access to extensive laboratory and national computational resources and has significant
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growth, electricity usage, and their implications for U.S. supply chains and energy infrastructure plans. The successful candidate will apply methods from economics, supply chain risk analysis, and data
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer
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spectrometers at the Advanced Photon Source. The successful candidate will work at the interface of cutting-edge cryogenic detector technology and synchrotron science, helping to integrate TES spectrometers
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will receive full consideration. Key Responsibilities AI-ready data and analysis for the ePIC Barrel Imaging Calorimeter and our Jefferson Lab program Support for the PRad-II and X17 experiments
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experimentalists, modelers, and data scientists Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of Materials Science, Chemical Engineering, Chemistry, or a related field