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develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work with the world's first exascale system
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of scientific AI. Focus Areas: Cross-Domain Interoperability: Develop common readiness templates, standardized metadata models, and APIs to enable seamless integration across diverse scientific domains
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modeling techniques and make fundamental contributions to the field. Interact with other researchers, technicians, and students to shape and drive the research agenda. Present and report research results and
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Requisition Id 15625 Overview: We are seeking a Postdoctoral Research Associate to advance modeling and AI-driven analysis for magnetic quantum materials, with a focus on neutron scattering and
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research analysis on geothermal well development and other advanced energy technologies that could achieve transformative gains in energy efficiency. Ability to develop optimization and life cycle models
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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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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such as quantization, model pruning, approximate attention (linear and sparse) and proposing new mechanisms for tackling speed, accuracy, as well as energy issues, for large language mode (LLM) inferencing
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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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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific