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Scarcity and Quality Dynamics: Investigate methods for addressing sparse labels, non-standard metadata, and imbalanced datasets to improve AI training robustness across scientific domains. Privacy and
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to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery. Publish research findings in high-impact journals and present results
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solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing efficient techniques that maintain robust privacy
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liquids, frustrated magnetism, excitonic magnets, and strongly correlated electron systems. You will work closely with theorists, experimentalists, and computer scientists to build robust, scalable
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uniting national laboratories, academic institutions and industry partners, the QSC is endeavoring to advance American innovation and global leadership by enhancing the computational robustness, algorithmic
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, high performance computing and deep learning. The candidate will work in a collaborative research and development environment focusing on designing, implementing, and applying robust and high performance
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, heterogeneity-aware scheduling, robust and efficient training and fine tuning. Contribute to open-source software, datasets, and standardized evaluation suites; mentor interns and students. Communicate results