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. Capability to follow and help develop testing and assembly procedures. Competence in reading and interpreting engineering component and assembly drawings. Solid foundation in mathematics, science, mechanical
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crystallization reactions, in aqueous solutions and at solid-water interfaces. Excellent record of productive and creative research demonstrated by publications in peer-reviewed journals. Excellent written and oral
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analytics that enhance and evolve business operations and scientific decision-making capability and related activities at ORNL.Qualified applicants will have a solid foundation of Generative AI and Machine
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superconducting materials is highly desirable. Experience with solid modeling and finite element analysis codes as it relates to electromagnetic interactions of coil assemblies, the modeling of cryogenic
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broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security challenges facing the nation. Major Duties/Responsibilities: Duties
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interesting ground states and behaviors. Lead the writing of research papers resulting from your work with support from the group and collaborators, and present research results at scientific conferences
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which are dependent on the situation (deskside visits, phone discussions, email, or by using remote tools). Performing other related duties as required. Contributing to Knowledge Base articles by
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
and 2D memristive materials). As a Postdoctoral Research Associate, you will contribute to research in these areas, bridging state-of-the-art atomistic and mesoscopic simulation methods as indicated
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the nation. The Advanced Engineering Technologies (AET) Group is seeking a dedicated CAD drafter/designer to support our organization. The position will involve machine design and process engineering in a
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security challenges facing the nation. We are seeking a Machine Learning (ML) Research Engineer who will support the development of self-supervised learning methods for large vision-language models