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Research Associate to develop and apply scalable artificial intelligence (AI) / deep learning (DL) methods to advance multi-scale coupled physics simulations in support of the missions and programs of the US
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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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, 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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(e.g., deep learning, implicit neural representations, diffusion models) for CT reconstruction, enhancement, and defect detection. Advance algorithms for multi-modal tomography (X-ray, neutron, electron
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the following areas are required: 1) wetland science; 2) hurricane science; 3) remote sensing; 4) deep learning and AI, 5) high-performance computing. Experience using AI models is required; experience
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, reinforcement learning with human feedback (RLHF), and agent-based orchestration for analyzing unstructured operational data. The position further encompasses several problems related to AI for Operations, where
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. Strong programming skills. Familiarity with popular Deep Learning platforms such as PyTorch and TensorFlow. Preferred Qualifications: Expertise in vision transformer or large language model. Expertise in
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Substantial programming skills using Python or modern C/C++ Experience with machine learning and deep learning libraries Experience building AI models in platforms such as TensorFlow, Keras, or PyTorch