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external partners Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of physics, chemistry, or physical chemistry Demonstrated expertise in theoretical quantum
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recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Computational Materials Science, Chemical Engineering or a closely related field. 2. Technical Expertise
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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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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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(NUCLEI); and two Topical Collaborations: 1) 3D quark-gluon structure of hadrons: mass, spin, and tomography, and 2) Nuclear Theory for New Physics. Further information on our group and its research
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to assess evolving risks in coastal-urban regions. Other key responsibilities include: Mesh design and high-resolution data utilization. Develop and refine high-resolution barotropic ocean meshes along U.S
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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
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The Data Science and Learning Division (DSL) of the Computing, Environment and Life Sciences Directorate (CELS) and the Materials Science Division (MSD) of the Physical Sciences and Engineering
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. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments. Experiments with Argonne involvement include, but are not
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experiments and corresponding data analysis. Following the successful demonstration of the technique, the candidate will collaborate with team members from material science to apply these methods to scientific