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, materials synthesis, and separation processes. Position Requirements Recent or soon-to-be-completed PhD (typically within the last 0–5 years) in chemistry, geochemistry, chemical engineering, physics
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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 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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PhD (within the last 0-5 years) in field of physics, chemistry, materials science, electrical engineering, or a related field Demonstrated expertise in electronic structure theory Experience with large
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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
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emphasize multi-wavelength survey science, the galaxy-halo connection, cluster cosmology, and large-scale cosmological simulations. Analysis efforts cover topics such as CMB power spectra, CMB lensing, galaxy
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at technical conferences. Position Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in mechanical engineering, materials science, civil engineering, computer science
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models Disseminate research through publications, presentations, and open-source contribution Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in Materials Science, Data
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contribute to open-source code repositories and documentation. Position Requirements Required skills, knowledge and qualifications: PhD in physical oceanography, coastal engineering, computational science
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The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting-edge computational and systems biology research. The primary focus