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CPU and GPU based HPC systems. Exploration of the capabilities of DPU/IPU SmartNICs to support network security isolation, platform level root-of-trust, and secure platform management/partitioning
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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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/Responsibilities: Developing and validating high fidelity whole building energy modeling Performing experiments in a test facility and experimental data analysis Developing and deploying AI based advanced control
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: https://www.ornl.gov/content/research-integrity Basic Qualifications: To be eligible you must have completed a PhD in chemistry, physics, engineering, or a related field with in the last 5 years
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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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PhD in materials science and engineering, physics, chemistry, or electrical engineering or a related field. Preferred Qualifications: Experience in scanning transmission electron microscopy Background
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and
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team of scientists to develop in situ monitoring technologies for DED-based AM processes. Design, integrate, and deploy multi modal sensor systems (including but not limited to optical, thermal, acoustic
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to optimize utility of captured signals Conceive, write, and submit proposals to develop and expand a research program investigating signal collection and analysis for mission objectives Qualifications: A PhD
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a particular emphasis on error-corrected methods for future fault-tolerant quantum computing. The algorithms will be designed to address key models of quantum materials, such as the Hubbard model