114 assistant-and-professor-and-computer-and-science-and-data uni jobs at Brookhaven Lab
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of the ATF to steward its unique science and technology user program. The AFD Director will directly support the Chair of the Accelerator Science and Technology (AS&T) Department to provide scientific
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qualification evaluation: Experience with the Incident Command System. Knowledge of basic computer skills. Environmental, Health, and Safety Requirements: Medical/physical requirements : Successfully pass a pre
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applies platforms for state-of-the-art techniques for Accelerated Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a
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curiosity with passion. Compliance with DOL Requirement involving H-1B applications. Current BNL employees, please Click Here for additional LCA information. Frequently Asked Questions about applying for a
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loads, in order to ensure safety of workers and materials; use hand signals and other means to direct crane operators and help guide the objects into place; and tilt and turn suspended loads to maneuver
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for the EIC project Assist to deliver EIC pulsed power systems and components from initial concept through engineering analysis, detailed electrical, electronic and mechanical design, design, system electrical
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documents. Ability to sufficiently write/complete work orders and other required documents. Ability to communicate verbally with customers, fellow workers and supervisors Possess basic computer skills. Must
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the equivalent tools associated with the EIC Project scope. Required Knowledge, Skills, and Abilities: BA/BS Degree (or equivalent experience), preferably in Computer Science or a related discipline At least ten
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Job Summary Organization Overview The Facilities & Operations (F&O) Directorate’s mission is to support the science and technology and environmental restoration missions of the Laboratory by
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Nanomaterial Discovery, integrating synthesis, advanced characterization, physical modeling, and computer science to iteratively explore a wide range of material parameters. The CFN develops and utilizes