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BNL staff (i.e., employees, guests, users, visitors, and contractors) are trained and qualified to perform their assigned tasks and job functions. In this role, you will support the analysis, design
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. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized
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. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized
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, Skills, and Abilities: Bachelor’s and 8+ years, or Master’s and 6+ years, or Doctorate and +2 years post-PhD. in the field of cell biology, plant biology, structure biology, immunology, biochemistry
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support the analysis, design, development and implementation of safety and compliance training courses and programs. Responsibilities include, but are not limited to: POSITION DESCRIPTION: The Technical
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for the characterization. Prepare research based manuscripts for publication Participate in the meetings related to the EFRC Required Knowledge, Skills, and Abilities: PhD in Condensed Matter Physics, Materials Science
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to deliver a unique science and technology research program. Maintain BNL’s strong international reputation for accelerator user facilities and innovation to enable ground-breaking fundamental science
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optimization. Basic knowledge of integrated circuit design, including digital simulation and logic synthesis. methods, and other related topics pertaining to fast AI model inference. Experience working in
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inversion methods, relevant to co-designing of interference EUV masks. •Implement appropriate AI/ML methods. •Perform coherent x-ray scattering, imaging and lithography experiments to validate and improve
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. The EIC will be a discovery machine for unlocking the secrets of the “glue” that binds the building blocks of visible matter in the universe. The machine design is based on the existing and highly optimized