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collaboration and innovation. Position Requirements This level of knowledge is typically achieved through a formal education in Statistics, Machine Learning, Computer Science, Logistics/Supply Chain, or a related
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-completed Ph.D. within the last 0-5 years in Atmospheric Science, Meteorology, Climate Science, Applied Mathematics, Data Science, or a related field with strong quantitative and computational research
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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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in materials for electrochemistry. While the focus in on computational expertise, this position will involve some experimental work in adapting workflows for automation and artificial intelligence
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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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fluorescence tomography at beamline 2-ID-E and 2-ID-D with focus on bioimaging of soil aggregates, as well as computational framework for modeling and reconstruction of related 3D datasets. The successful
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interactions, or aerosol–cloud interactions Strong experience in numerical modeling and high-performance computing • Experience applying AI/ML methods to model development, with strong programming skills (e.g
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information science and light–matter engineering, while engaging with CNM’s cleanroom and characterization capabilities, APS ultrafast and nanoprobe X-ray beamlines, MSD’s THz initiatives, and Q-NEXT’s national quantum
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The Surface Scattering and Microdiffraction (SSM) group in the X-ray Science Division (XSD) at the Advanced Photon Source (APS), Argonne National Laboratory is seeking Two Postdoctoral Appointees, both focused on multimodal synchrotron characterization of defects and interfaces in oxides and 2D...