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Field
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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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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 6 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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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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 6 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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Assistant (RA) or a Postdoctoral Research Associate (PDRA). The appointed candidates will support advanced research initiatives focusing on systems design, distributed systems, and algorithmic optimization
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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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intelligence, and distributed AI, can enable future healthcare applications. This includes novel approaches for medical sensing, physiological monitoring, diagnostics, therapy support, and telemedicine
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/cee. We are looking for a Research Fellow, Distributed Acoustic Sensing to advance research on distributed fiber-optic sensing for infrastructure, urban, and environmental applications. The role will
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the crossover between artificial intelligence and Archaeological Prospection. Archaeological prospection faces an unusual combination of challenges: data are sparse and unevenly distributed, observations