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the following areas is preferred, and an exceptionally strong candidate in a single area is also encouraged to apply. Relevant areas include: Parallel and distributed graph and or ML algorithms
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 9 days ago
contribute to the design, development, and experimental validation of novel graybox tunneling algorithms for multi-objective combinatorial optimization, with a particular focus on their parallelization and
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at the intersection of AI systems, distributed and high-performance computing, edge-to-cloud platforms, and domain-driven applications such as digital agriculture and environmental intelligence. The Postdoctoral
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. Knowledge of HPC matrix, tensor and graph algorithms. Knowledge on distributed algorithms using MPI and other frameworks such as NCCL. Knowledge of high-performance computing and its applications. Special
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collection, data storage, algorithms, and AI learning principles that allow components of a distributed AI system to be trained collaboratively to dynamically better cooperate toward a unified production
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participate in projects using passive acoustic monitoring to assess soundscapes, anthropogenic noise, and the distribution, occurrence, movements, and density of marine mammals in the Gulf of Mexico, North
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
QCD. The successful candidate will work with Prof. Kostas Orginos on first-principles calculations of hadron structure, including parton distribution functions, generalized parton distributions, and
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members Sriram Pemmaraju and Sourya Roy on sampling problems in the distributed and parallel computing setting. The ideal candidate will have research experience in sampling algorithms and related areas
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Earth observation systems, enabling coordinated, event-driven operations across heterogeneous space assets through onboard intelligence, distributed computing and inter-satellite communications. By
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 29 days ago
regions where ground radar and gauge networks are sparse. In the reverse direction, high-resolution precipitation fields (e.g., MRMS) provide spatially distributed validation for fine-scale soil moisture