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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours 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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 22 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
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 22 days ago
. Description: Satellite limb and occultation observations provide a unique opportunity to derive critical information about the vertical distribution of aerosols and trace gases with high vertical resolution
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-on learning and research opportunities. The Postdoctoral Research Associate will contribute to the development of algorithms and software that enable teams of robots to perform coordinated maneuvers, self
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 22 days ago
gradients, and geomorphic transience regulate rock-derived nutrient (P, Ca, Mg, K, and bedrock N) distributions across landscape to cross-basin scales. Agricultural & Agroecosystem Dynamics: Quantifying crop
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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. Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions. Knowledge of federated learning SOTA algorithms. Knowledge of distributed
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). The successful candidate will contribute to the modeling, simulation, and co-design of next-generation Quantum-HPC (QHPC) architectures, with particular emphasis on integration of quantum and HPC distributed