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machine learning, deep learning, foundation models, agentic AI systems, graph neural networks, and knowledge‑graph–based reasoning. Familiarity with integrating AI into scientific workflows at scale
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mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design
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from plant genomics to phenomics with biological mechanisms embedded in deep neutral networks. GPTgp will allow task-specific training and transfer learning across reactions, pathways, biodesigns, and
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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