91 computational-physics "https:" "https:" "https:" "https:" "INRAE" positions at Argonne
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. This position offers an exciting opportunity to contribute to fundamental and applied research in materials chemistry using advanced computational techniques and artificial intelligence. The project involves: 1
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
, physics-informed AI agent that accelerates discovery in catalysis science—particularly for the CO₂ reduction reaction (CO₂RR) and oxygen evolution reaction (OER). The postdoc will design and perform
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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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lead efforts to develop experimental techniques using conventional and coherent imaging in the ultrafast time domain, as well as a computational framework for modeling and reconstructing images
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, interdisciplinary environment with access to large-scale computing resources and diverse scientific use cases. The position strongly supports publishing in top-tier venues, contributing to open-source research
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or soon-to-be-completed PhD (typically completed within the last 0-5 years) in physics, chemistry, or materials science with 0 to 2 years of experience, or the equivalent experience through practical
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, distributions, and dynamics in metallic, oxide, and semiconducting systems. This project integrates high-throughput and in situ TEM experimentation with AI/ML-driven image analysis and computational modeling
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facilities in Argonne National Laboratory, including the Advanced Photon Source and the Center for Nanoscale Materials, and integrates expertise in ultrafast optics, accelerator physics, and condensed-matter
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interdisciplinary teams within the Materials Science division at the Argonne National Laboratory and external collaborators. Position Requirements • Ph.D. (completed or soon to be completed) in Physics, Materials
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experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy