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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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for electro-optic modeling Desirable Skills Data analysis using Python Experience with autonomous or AI-assisted synthesis workflows Familiarity with quantum transduction or quantum information science
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to ensure quality data. Communicate effectively with supervisors, peers, and Laboratory management through status updates, technical research reports, project presentations, and other regular channels
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related field Strong molecular biology skills (cloning, vector design, transformation), protein and nucleic acid prep-scale purification and analysis, and quantitative data analysis Excellent communication
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or equivalent in the scientific application of this knowledge and practical laboratory experience. Skill in devising and performing experiments to acquire identified data, using and maintaining research equipment
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The Materials Science Division (MSD) of Argonne National Laboratory is seeking applicants for a postdoctoral appointee in atomic layer deposition of thin films, in situ metrology, interface science
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in computational science, machine learning, and experience with synchrotron data analysis are strongly encouraged to apply. Position Requirements PhD completed in the past 5 years or soon to be
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, Astrophysics, Physics, Cosmology, or a related quantitative field (e.g., Applied Mathematics, Computer Science, Statistics, Data Science) Demonstrated research experience in observational cosmology or wide-field
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The Argonne Leadership Computing Facility’s (ALCF) mission is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing
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simulations, design and conduct experiments, and analyze multimodal data streams in a continuous, real-time loop with minimal human intervention (https://www.nature.com/articles/s41524-024-01423-2 , https