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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 15 hours 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 23 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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. 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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 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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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