63 physics-engineer "https:" "https:" "https:" "https:" "https:" Postdoctoral positions at Argonne
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The Chemical Sciences and Engineering Division (CSE) at Argonne National Laboratory invites applications for a Postdoctoral Appointee position focused on advanced battery materials research
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engineering, or related scientific background. Experience with electrochemistry, electrochemical analysis, and electrodeposition of metals, in contact with aqueous, non-aqueous, and molten salt electrolyte
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Position Requirements Ph.D. in Materials Science, Physics, Electrical Engineering, Applied Physics, or a related field (completed or soon-to-be-completed) Demonstrated expertise in nano- and mesoscale
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and engineers across Argonne, including the Materials Engineering Research Facilities (MERF) and the Argonne MXene Innovations (AMI) program, while collaborating with industrial and academic partners
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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 past five years or soon-to-be completed in physics, materials science, chemistry, engineering, or a related discipline. Demonstrated expertise in one or more synchrotron X-ray methods such as BCDI, XPCS
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The High Energy Physics Division at Argonne National Laboratory (ANL) invites applications for a Postdoctoral Research Associate position to join our team working on the ATLAS experiment
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PhD (within the last 0-5 years) in field of materials science, chemistry, chemical engineering, computer science, or a related field Experience operating and troubleshooting laboratory automation
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Science and Engineering, Applied Physics, or a closely related discipline. • Demonstrated expertise in time-resolved X-ray diffraction and in-situ X-ray micro/nanoscopy. • Experience working with
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together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for multimodal federated learning