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://arxiv.org/abs/2509.00098 ) This project sits at the intersection of artificial intelligence and materials characterization and modeling. The goal is to create an AI system that can intelligently operate
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involve synthesis of novel crystalline materials using molecular beam epitaxy (MBE) and characterization and modeling using a variety of transport and optical techniques, in close collaboration with a team
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to the Lab’s broader effort in CH4 and CO2 utilization R&D. The role will require the individual to work with personnel that perform machine learning and molecular simulations and electrochemical device testing
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of findings to the community through publications and presentations. Position Requirements Ph.D. in Molecular Biology, Biochemistry, Biotechnology, Plant Biology, Protein Engineering, Microbiology, or a related
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contributions in: Building novel generative models for predicting genome-scale evolutionary patterns using GenSLMs Developing scalable models that can, when integrated with high throughput molecular dynamics
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Position Requirements • Recent or soon-to-be-completed PhD (within the last 0-5 years) in the field of organic, organometallic, or inorganic chemistry, or a related field • Ability to model Argonne’s core
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to understand molecular-scale processes occurring at aqueous-electrode interfaces associated with water purification in the AMEWS EFRC. This will be achieved through the use and application of various interfacial
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of molecular and/or heterogeneous catalysts Strong understanding of kinetics and thermodynamics as applied to catalysis Knowledge of and experimental expertise in synthesis and characterization of highly air
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of molecular reactions occurring at the surface of various materials. In addition, computational fluid dynamics (CFD) simulations combined with microkinetic modeling will be carried out to study the heat
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information Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork Desired skills, knowledge and abilities: Experience with large-scale molecular dynamics (MD) simulations