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The Theory and Modeling Group at the Center for Nanoscale Materials (CNM) seeks an outstanding Assistant Scientist to lead and support frontier research at the intersection of AI/ML, data
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, materials science, or a related discipline Background and/or interest in one or more of the following areas: critical elements and materials, electrical double layer theory and applications, solid–liquid
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Physics, Materials Science, Chemistry, Chemical Engineering, Applied Physics, or a closely related field with a focus on computational materials modeling. Density Functional Theory (DFT) for surfaces and
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areas is preferred and will help the candidate succeed: X-ray absorption spectroscopy theory and modelling, and handling large synchrotron/X-ray datasets. Experience with vector databases (e.g. Pinecone
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familiarity with ML frameworks such as PyTorch, Jax, or TensorFlow. A strong foundation in statistical methods, probability theory, or uncertainty quantification is highly advantageous. Job Family Postdoctoral
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deposition (ALD). The project involves performing quantum mechanical calculations (e.g., first principles density functional theory (DFT)) to identify the structures and to understand the complex mechanisms
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in multidisciplinary teams. Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork. Preferred Knowledge, Skills, and Experience Background in economic theories and
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Knowledge, Skills, and Experience: Proficiency in mathematical analysis and operator theory. Experience working with microelectronics. Experience in conducting synchrotron experiments and analyzing
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physics, nuclear theory, nuclear data, accelerator R&D and operates the DOE Office of Nuclear Physics National User Facility, the Argonne Tandem-Linac Accelerator System (ATLAS). As an equal employment