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Field
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 1 hour 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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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 9 hours ago
algorithm performance. You will analyze assignment data from December 2025 to the present to identify workload distribution patterns and refine the algorithm using mathematical modeling, programming, and
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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day 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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partners to identify and characterise relevant seismic sources Develop PSHA seismic-source models covering source geometries, distributed-seismicity zones, smoothed seismicity, three-dimensional shallow
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learning, large-scale model optimization, and generalization. To explore scalable optimization methods for large-scale, distributed, and multi-node collaborative training. To conduct theoretical analysis
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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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identification through lab-scale and field experiments. Key Responsibilities: Develop algorithms for guided-wave analysis, response analysis, sensor fusion, and system identification using distributed and multi
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follow specialized training programs (e.g. ARENA). Specific Requirements Knowledge: Linear algebra, probability and statistics. Graph theory and algorithms on graphs. Machine learning and deep learning
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 21 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