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Perform advanced characterization using synchrotron-based X-ray techniques to investigate material behavior and interfacial chemistry during synthesis and battery operation Develop mechanistic understanding
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conditions to fabricate membrane components with desired physics and ionic transport properties. As part of the Materials Engineering Research Facilities (MERF), the Nanocomposite Materials and Membrane
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. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary responsibilities will be
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an independently-funded research program within 2-3 years. The successful candidate will develop and apply advanced data-driven methodologies to accelerate discovery in materials/chemistry design, characterization
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preservation (e.g., glycerol stocks, subculturing), aseptic technique, and operation under BSL-1 and BSL-2 containment requirements. Experience with microbial molecular biology techniques, including DNA/RNA
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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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experiments. Experience: Ph.D degree in the field of Condensed Matter Physics, experimental High Energy Physics, Quantum Information Science, or a closely related discipline. Demonstrated experience in quantum
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is supported by a DOE-funded research program on ultrafast science involving Argonne National Laboratory, University of Washington, and MIT. The goal of this research program is to understand and
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, we encourage applicants to consider how their data processing and software development capability fits into the proposed work and future direction. The Postdocs will be working directly with assigned
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-ion battery recycling processes. This will require innovative approaches to solve challenges that go beyond the use of pilot scale or commercialized equipment. As such we are seeking candidates