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systems. In parallel, they will design and develop agentic AI and physics-aware AI models to accelerate discovery and deepen mechanistic insight in catalysis. This work will be carried out in close
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postdoctoral appointee to develop computational fluid dynamic (CFD) tools that make exascale computing accessible to a broader set of users. The successful candidate will develop a massively parallel
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PhD and undergraduate students, prepare technical reports and manuscripts, and support future project applications. Major Duties / Responsibilities: Advance synthetic techniques to develop existing
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usefulness for accelerator operation. Position Requirements PhD completed in the past 5 years or soon-to-be completed in accelerator physics or a related field is strongly preferred. PhD graduates in other
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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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research program at the interface of theory and experiment. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physical chemistry, inorganic chemistry
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metallization. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemistry, materials science, metallurgy, electrochemistry, electrometallurgy
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-completed PhD (within the last 0-5 years) in field experimental physics, engineering, or a closely related field Excellent written and verbal communication skills Demonstrated ability to work effectively in
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-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, structural engineering, or a closely related field. Expertise in formulating
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Facility (MERF), 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