88 computer-science-intern-"https:"-"https:"-"https:" Postdoctoral positions at Argonne
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. Position Requirements A formal education in Physics, Materials Science, Chemistry, or a related field at the PhD level with zero to five years of employment experience. Demonstrated experience with high
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The Materials Science Division (MSD) at Argonne National Laboratory is seeking highly motivated applicants for a postdoctoral appointee to join a multidisciplinary team developing next-generation
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materials from complex feedstocks to achieve the desired product quality and form. As a part of this team, you will: Apply electrochemical engineering principles to develop processes such as oxide reduction
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years ) Ph.D. in Engineering, Operations, Computer Science, Mathematics or a related field. Knowledge of optimization, power systems operations and planning, electricity markets, issues surrounding
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limited to, ATLAS at CERN, the South Pole Telescope, and the Simons Observatory. The candidate is also expected to work closely with computational experts at the Computational Science (CPS) division
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, Quantum Information and Quantum Simulation. The successful candidate will be expected to carry out an independent and collaborative research program in particle theory that strengthens and complements
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Postdoctoral Appointee - Investigation of Electrocatalytic Interfaces with Advanced X-ray Microscopy
We are seeking a Postdoctoral Research Associate to join the Microscopy Group in the X-ray Science Division (XSD) at the Advanced Photon Source (APS), Argonne National Laboratory. This position is
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-completed PhD (typically completed within the last 0-5 years) in chemical engineering, environmental engineering, or similar degree. Experience with data collection, processing, analysis, and presentation
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The Chemical Sciences and Engineering Division is seeking a highly qualified and motivated postdoctoral researcher to join our team in the area of light-matter interactions, with a particular focus
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programming, interfacing hardware, and developing machine-learning methods highly desirable. The researcher will join an Argonne funded project with interdisciplinary team of material scientists, computer