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The postdoctoral fellow will contribute to ongoing research projects exploring genetic interactions that influence lifespan, assist in experimental design, data analysis, and publication of findings. Additional
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design, develop, and evaluate AI-driven scientific visualization assistants that support intuitive, context-aware interaction with large-scale simulation and experimental data. The postdoc will focus
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research and development needs within the MDF Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote
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, presentations, reports, and code documentation. Align behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a
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learning analytics, with a focus on the deployment of eye-trackers or fNIRS devices; creating end-to-end prototypes of AI systems, including LLM integration, backend development, and interface design and
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interdisciplinary scientific experience. Our approach integrates cutting-edge tools and technology, empowering researchers to push the limits of knowledge and innovation. With us, your curiosity will know no bounds
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groundbreaking solutions for a healthier society in a rapidly changing world. We believe that diverse perspectives drive innovation. Through strong partnerships, we accelerate the transfer of new ideas from
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, design, and performance analysis of emerging massive multiple-input multiple-output (MIMO) architectures for next-generation wireless communications systems. The candidate will investigate advanced MIMO
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | about 2 hours ago
. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform