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The Materials Science Division is seeking applicants for a Postdoctoral Appointee who will conduct cutting-edge research in AI for Materials Chemistry, with a focus on energy storage and conversion
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synchrotrons and x-ray free-electron lasers. Key Responsibilities Perform electronic-structure calculations using ab initio quantum chemistry methods and software (commonly CASSCF-based approaches) Investigate a
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materials complexation and conversion, and energy storage and conversion. This position offers an exciting opportunity to contribute to fundamental and applied research in materials chemistry using AI
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simulations. The candidate is expected to work as part of a collaborative team of chemists, physicists, and engineers to develop new chemistry for upgrading hydrocarbon streams. The candidate should be
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, including synthesis of fuel materials and recycling of used MSR salts. The selected candidate will develop and optimize process chemistries to synthesize chloride and fluoride species, recover metals and
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-completed PhD (within the last 0–5 years) with strong background in physical chemistry, photophysics, photochemistry, and polariton chemistry Hands-on experience in thin-film deposition, and spectroscopic
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projects in artificial intelligence, materials engineering, chemistry, and beyond at Argonne National Laboratory. Position Requirements Recently completed PhD within the last 0-5 years in computer science
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, primarily for recycling used nuclear fuel to support the deployment of advanced reactors. The selected candidate will develop and optimize novel separations chemistries to recover actinide and rare earth
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Science, Chemistry, Chemical Engineering, Electrical Engineering, Computer Science, Physics, or a related field Demonstrated proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow
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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