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kinetics for molten salt systems Communicate effectively with supervisors, peers, and Laboratory management through status updates, technical research reports, project presentations, peer-reviewed
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, project presentations, and other regular channels. Position Requirements Skill in modeling, processing, and analyzing computational results to inform accompanying experimental efforts. Skill in the use
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, project presentations, and other regular channels. Position Requirements This level of knowledge is typically achieved through a formal education in chemical engineering, mechanical engineering, or a
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skills and qualifications: A recent PhD (completed within the last 5 years) in computer science, electrical engineering, or a related field. Strong background in network interconnect design and
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and scalability Collaborate with interdisciplinary teams to design future data-management solutions This position will play a crucial role in enhancing ALCF's capabilities in supporting AI-driven
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(predoctoral) or PhD (postdoctoral) in Materials Science, Chemistry, Physics, or related area is required. Coursework in computer science or data science is desirable. Familiarity with research data management
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applications in beamline science and high energy physics, as well as interaction with device and materials researchers. Primary responsibilities will be to design and implement new methods to deploy, monitor
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The Materials Science Division (MSD) at Argonne National Lab (ANL) is looking for an experimental postdoctoral candidate to conduct fundamental research in the areas of optically addressable atomic
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management through status updates, technical research reports, project presentations, and other regular channels. Develop technical ideas and proposals to advance the understanding of molten salt
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computational science expertise. The ALCF has an opening for a postdoctoral position in data management targeting AI applications at scale. This Postdoc will join the AL/ML group, a vibrant multidisciplinary team