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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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biomedicine and health. It provides foundations and advances in quantum information sciences to enable quantum computers, devices, and networked systems. It develops community applications, data assets, and
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of next-generation semiconductor devices. The candidate will collaborate with researchers across CNMS, ORNL, and the broader NSRC network working in materials synthesis, device fabrication, theoretical
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applied AI/DL research in support of the Laboratory’s missions. Basic Qualifications: A PhD in computer science or an AI-related field completed within the last 5 years. Demonstrated expertise in scalable
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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success. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, Data Science, or a related discipline completed within the last three years. An excellent record
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such as classifier free guided diffusion models, transformers with multi-headed attention, physics-informed neural networks, materials foundational models with multi-task learning, symbolic regression
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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