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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and
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photosynthesis to join the new pilot study of Generative Pretrained Transformer for genomic photosynthesis (GPTgp). The GPTgp project aims to develop a foundational holistic model of photosynthesis that will scale
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include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and iterative solvers. Successful applications will work in applications
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and transient inverter modeling and different applications of the simulation. Selection will be based on qualifications, relevant experience, skills, and education. You should be highly self-motivated
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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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applications for biomedical research. The candidate’s work will focus on developing AI methods, training AI models, and creating agentic AI workflows on DOE supercomputers and applying them to population-level
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, and
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for Risk Forecasting, Large Language Models (LLMs), and Human-in-the-Loop AI Systems. Our aim is to advance AI for Operations by integrating next-generation AI agents and LLMs with real-world operational
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AI processes (e.g., model training, inference). Develop agentic AI systems and AI harnessing techniques to enhance model quality, resource optimization, and adaptive execution in diverse workflows
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breaking in nature, the limits of nuclear stability, and signatures of new physics beyond the Standard Model. Major Duties/Responsibilities: Develop formalism and methods for computing properties of nuclei