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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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-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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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 solver, capable of running
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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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help shape technologies with broad relevance to future quantum systems. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physics, electrical engineering
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. Position Requirements A formal education in materials science and engineering, nuclear engineering, or a related field at the PhD level with zero to five years of employment experience. Knowledge of metallic
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requires hands-on laboratory work to develop and synthesize and analyze new organic materials, specifically new battery electrolyte materials. Position Requirements Recent or soon-to-be-completed PhD
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. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemistry, materials science, metallurgy, electrochemistry, electrometallurgy, chemical
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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 branches