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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
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sparse algorithms. The successful candidate will contribute to advancing secure, trustworthy, and efficient AI solutions for scientific applications. Key responsibilities include developing state
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration
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and Electronics group in the Electrification Section of EEID/ ESTD, at Oak Ridge National Laboratory (ORNL). Major Duties/Responsibilities: Develop and deploy algorithmic tools for monitoring
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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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to bear as you develop new methods to address scientific and engineering problems, collaborate with leaders in your field and across the laboratory, while working with the world’s fastest computers, and
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on the development of new algorithms and methods that can be used to analyze large and complex scientific data using novel computer architectures such as high performance, neuromorphic, and quantum computing. We are a
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conditions, identification of vulnerabilities, and development of resilience enhancement strategies. Contribute to the design, development, and implementation of new models, methods, and algorithms