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techniques to solve pressing challenges in energy storage. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne
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physics (HEP) and nuclear physics (NP) experiments. The successful candidate will be a key member of a multidisciplinary co-design team integrating materials science, computing, and device engineering to
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of radiofrequency (MHz–GHz) nanoscale phenomena in systems relevant to microelectronics and quantum information science. Opportunities also exist for cross-platform studies integrating ultrafast TEM with ultrafast x
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preparation of manuscripts, reports, and presentations; present findings at internal meetings and external conference Collaborate in a multidisciplinary team environment spanning experimentalists, theorists
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Argonne National Laboratory’s Accelerator Science Division is seeking a Postdoctoral Appointee to contribute to the development of a Sub- THz Collinear Structural Wakefield Accelerator
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looking for candidates whose research program aligns with the 2023 Long Range Plan for Nuclear Physics, focusing on lab-based tests of fundamental symmetries via precision experiments. The ideal candidate
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, asphaltenes, resins, and kerogen molecules and contribute to engineering design of upscaled processes. The candidate will be a part of the Applied Materials Division (AMD) within AET at Argonne and will
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primary goal of this work is aimed at advancing next-generation, lithium-ion technology through a detailed understanding and mitigation of surface degradation mechanisms that limit state-of-the-art lithium
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The Data Science and Learning Division (DSL) at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting edge molecular and microbiology work to enhance non-proliferation
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capable of hypothesis generation, experimental design, data analysis, and iterative learning in biological contexts. This project seeks to transform how computational and experimental biology research is