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://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/file Equal Opportunity/Affirmative Action Employer Brookhaven Science Associates is an equal opportunity employer that values inclusion and
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The Data Science and System Integration (DSSI) Division of the National Synchrotron Light Source II (NSLS-II) is responsible for ensuring reliable controls and data infrastructure enabling users
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the operation, software, and computing of the ATLAS experiment. Required Knowledge, Skills, and Abilities: Ph.D. in experimental particle or nuclear physics or related field. Experience with data analysis and
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sewerage systems, related facilities and auxiliary equipment. The hours of operation/shift for this position are M-F 8am-4:30pm. All mechanics shall work weekends, holidays, and overtime as required
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transformations, quantum information science, microelectronics and energy storage. The research will involve the use of pulse radiolysis at the Chemistry Division’s 9 MeV Laser Electron Accelerator Facility (LEAF
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. Knowledge of BNL practices, policies, and procedures. Environmental, Health & Safety Requirements: The incumbent will occasionally work protracted or irregular hours. Computer/keyboard work is an essential
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/400-series/0486.1-BOrder-a/@@images/file Equal Opportunity/Affirmative Action Employer Brookhaven Science Associates is an equal opportunity employer that values inclusion and diversity at our Lab. We
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to remove legacy items from EBNN Areas. Following BNL processes, properly dispose of research material and equipment (glass containers, tools, instrumentation, computers, carts, gas cylinders, lecture bottles
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found at: https://www.directives.doe.gov/directives-documents/400-series/0486.1-BOrder-a/@@images/file Equal Opportunity/Affirmative Action Employer Brookhaven Science Associates is an equal opportunity
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world-class program that advances the state of the art in artificial intelligence, high performance computing, applied mathematics, computer and computational sciences. BNL's distinctive domain science