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2019, the U.S. Department of Energy (DOE) approved the EIC project by issuing Critical Decision 0 (CD-0), or “mission need,” and selected Brookhaven National Laboratory (BNL) as the host site
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materials and their unique physical phenomena, such as metal-insulator transitions, charge-spin-lattice correlations, and the critical role of defects and interfaces. Utilizing state-of-the-art electron
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proposals for ongoing research program Required Knowledge, Skills, and Abilities: Ph.D. in physics or related discipline within the last 5 years Strong background in condensed matter physics Data analysis
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will collaborate closely with NSLS-II staff while developing cutting edge sample preparation and data analysis techniques that enable the next generation of the XCFS methodology. In addition
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complex terrain regions. CMAS does this by innovating on the fronts of meteorological data acquisition, analysis, and interpretation (https://www.bnl.gov/cmas/). The CMAS work portfolio is conducted within
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a related field Experience with radiation transport codes (e.g., FLUKA, Geant4, MCNP etc.) Excellent programming and data analysis skills (e.g., Python, C++, or similar) Solid understanding
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computational resources for data analysis. This position offers a dynamic, collaborative environment, engaging with experts across plant biology, microbiology, structural biology, and computational sciences and
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.) and electrical device data analysis including transistor characteristics. You communicate effectively, verbally and in writing, evidenced by peer-reviewed publications and conference presentations
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artificial intelligence (AI) and machine learning (ML) methodologies and interested in advancing these tools for accelerating the analysis of the big data acquired by electron microscopy. • You work
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and relevant data analysis. • Demonstrated experience in Python programming. • Knowledge of machine-learning algorithms. Additional Information: BNL policy requires that after obtaining a PhD