78 computational-physics-"https:"-"https:"-"https:"-"https:"-"BioData" Postdoctoral positions at Argonne in United States
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, instrumentation, modeling, and data science Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field(s) of materials science, physics, computational science, or a related field
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PhD (within the last 0-5 years) in field of physics, chemistry, materials science, electrical engineering, or a related field Demonstrated expertise in electronic structure theory Experience with large
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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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of reaction mechanisms in molten salts and apply insights to process development and scale up. Project activities will include the design and development of advanced sensors and flow systems for molten salts
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The Center for Nanoscale Materials (CNM) at Argonne National Laboratory seeks a highly motivated postdoctoral researcher to join a multidisciplinary team advancing quantum information
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multidisciplinary team, the candidate will work at the intersection of AI/ML, domain sciences, and high-performance computing. The role requires a strong foundation in LLMs and machine learning, along with
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. Position Requirements A formal education in Physics, Materials Science, Chemistry, or a related field at the PhD level with zero to five years of employment experience. Demonstrated experience with high
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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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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