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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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connect to our group’s work and how this position supports their career development goals. Possible research topics include (but are not limited to): Optimization algorithms for machine learning (stochastic
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quantum information, develop techniques for quantum control and measurement, build quantum computing hardware and software, and explore novel applications. Our main interest is to propose quantum algorithms
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projects are devoted to in-house algorithm and code developments driven by leading-edge scientific challenges and needs. The NanoSIMS lab is specialized for studies of presolar grains and ancient planetary
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algorithm and code developments driven by leading-edge scientific challenges and needs. The NanoSIMS lab is specialized for studies of presolar grains and ancient planetary materials. ASIAA has a dedicated
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atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address fundamental scientific challenges, including global cloud
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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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developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration and validation of results. Deliver ORNL’s mission by aligning
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Requisition Id 15448 Overview: We are seeking a Postdoctoral Research Associate who will focus on creating innovative artificial intelligence algorithms for the trusted visualization of large
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accurate completion. Algorithm Design: Design and implement algorithms for tensor completion, considering the unique challenges posed by sparse and multidimensional network data. This involves developing