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(completed within the last 5 years or nearing completion in 2026). Proven ability to conduct independent research or technical development, analyze complex systems, and publish results or deploy production
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of Large Language Models (LLMs) for scientific use cases. This position focuses on advancing LLM capabilities to address complex challenges across a range of scientific domains. As part of a
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range of molecular systems, including: Transition-metal complexes (e.g., chiral ruthenium and iridium complexes) Local and nonlocal inner-shell decay processes in solvated ions and transition-metal
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collaboration with team members. Skilled written and verbal communicator, including the ability to present complex information so that it is understandable to a broad audience. Computer skills relevant for data
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mechanisms of photoinduced charge transfer and spin state conversion across a wide spectrum of systems, including small transition metal complexes and donor-acceptor systems. By performing state-of-the-art
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optical and THz techniques. Ability to analyze and understand complex data set is required. Experience to lead ultrafast x-ray scattering or electron scattering experiments is a plus but not required
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Argonne National Laboratory is seeking a Postdoctoral Appointee to work in the Advanced Energy Technologies (AET) Directorate. The successful candidate will study chemical upgrading of heavy oils
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simulations and experiments across scientific user facilities, leveraging data to understand complex material phenomena across scales. Key Responsibilities Design, implement, and validate physics-informed AI/ML
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing