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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
Requirements Required skills, abilities, and knowledge: Recent or soon-to-be completed PhD (within the last 0-5 years) by the start of the appointment in computer science, electrical engineering, applied
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is typically achieved through a formal education in physics, mechanical engineering, or chemical engineering, or a related field at the PhD level with zero to five years of employment experience with a
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Argonne National Laboratory, a U.S. Department of Energy multidisciplinary science and engineering research center, is committed to finding solutions for national priorities, including advancing
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collaborative effort across XSD, the Materials Science Division (MSD), and the Chemical Sciences and Engineering Division (CSE). Key Responsibilities: Develop and enhance pump–probe sub-nanosecond TXS method
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discovery. Requirements: - A PhD in materials science or related science or engineering field received within the past 0-5 years. - Excellent written and oral communication skills as well as the ability
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Requirements Recent or soon-to-be-completed PhD (typically completed within the last 0-5 years) in chemistry, chemical engineering or materials science (those with other degrees but have similar skills to those
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experimentalists, modelers, and data scientists Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in field of Materials Science, Chemical Engineering, Chemistry, or a related field
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science, or a related engineering field is required. · To be eligible, candidates must have completed their Ph.D. within the last five years and must be available to start work by the end of 2025. Job
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of findings to the community through publications and presentations. Position Requirements Ph.D. in Molecular Biology, Biochemistry, Biotechnology, Plant Biology, Protein Engineering, Microbiology, or a related
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collaborating with a software engineering team to translate research into production-ready tools. The successful candidate will be part of an inter-lab, highly inter-disciplinary team of experts in ML, applied