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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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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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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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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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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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, Hugging Face etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers
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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
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, distributed control systems, AC power distribution, and DC low and high voltage systems. Coordinating hands-on assembly, functional testing, and troubleshooting of complex systems, including stable isotope
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partnerships with industry, universities, and other national laboratories Design and implement advanced sensing and controls algorithms for manufacturing Communicate research results through presentations
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algorithms. An understanding of when to apply what data-driven approach, from statistical techniques to generative AI models. Experience working with DOE National Laboratories (or similar R&D organizations