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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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. The directorate includes the Accelerator Science & Technology Department, the Instrumentation Department, the Superconducting Magnet Division (SMD), and the being-formed Quantum Information Science and Technology
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(HL-LHC) upgrade. BNL serves as the host laboratory for the U.S. ATLAS Operations Program, the U.S. ATLAS HL-LHC Upgrade Project, the ATLAS Tier-1 computing facility, and is an active U.S. ATLAS Center
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class science to occur in a safe and secure environment. The Police Group within LP, protects classified matter, special nuclear materials, hazardous materials, and property against theft, diversion
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the virtual Student Teaching Assistant will attend each lecture and lead their respective workshops to aid the students in successfully completing the program. Essential Duties and Responsibilities: Directly
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program completion at BNL + 6 years’ relevant experience or; a technical high school certification program (e.g. BOCES) + 7 years relevant work experience or; 8 years’ relevant work experience. You are
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materials for the next generation of microelectronics and quantum information science and technology. Position Description: We are looking for a Postdoctoral Research Associatefor a 1-year term , to grow high
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involvement programs that advance the science and science education missions of the Laboratory, contribute to the public understanding of science, enhance the value of the Lab as a community asset and ensure
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, cryogenic components, and water/air cooled systems. Join BNL at the beginning of this ground-breaking scientific and engineering project and become part of the team building this scientific discovery machine
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candidates should have a major in electrical engineering, computer science, or applied mathematics. A background in electric power systems modeling and simulation and data analytics and machine learning