43 distributed-algorithm-"Meta"-"Meta"-"Meta" positions at Oak Ridge National Laboratory
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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algorithms and parallel/distributed variational algorithms in AI/ML for application workflows and large-scale HPC and QC systems Develop quantum machine learning (QML) algorithms for optimization of multi
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training algorithms and AI architecture Image reconstruction, segmentation, and classification High performance computing for spatiotemporal data Major Duties/Responsibilities: Develop foundation AI models
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validate these distributed intelligence algorithms, enabling breakthroughs in scientific research across DOE domains. The candidate will collaborate with DOE’s SWARM project (https://swarm-workflows.org
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of the Algorithms and Performance Analysis (APA) group, work with NCCS users across a wide variety of computational domains to enable their efficient use of the world class facilities provided by the NCCS. We seek
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Requisition Id 15000 Overview: We are seeking an Associate R&D Staff member who will focus on algorithmic tools for process monitoring, diagnosis, and control. This position resides in the Sensors
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teams to codesign hardware, algorithms, benchmarks and software for QHPC systems, aiming to advance our strategic goals in leveraging quantum computing and high-performance computing (HPC) to develop
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on solving the world’s most challenging problems through artificial intelligence and data driven algorithms and systems. CSMD creates the mathematics, artificial intelligence, and architecture-aware algorithms
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learning including reinforcement learning, nature inspired algorithms, deep learning, agent based modeling, and natural language processing. We are specifically interested in research focused
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discovery with a strong emphasis on domain-driven impact. Develop, optimize, and transition algorithm prototypes to robust implementations Work with ORNL researchers, as well as internal and external project