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Field
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the Distributed AI for dependable cyberSecuritY (DAISY) Unit, of FBK-CS, which focuses on developing novel AI-driven methods for cyber threat detection in distributed computing environments, with a particular
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 3 hours ago
. Description: The project aims to understand the distribution of size, albedo, and orbital elements of the smaller (sub-kilometer) Main Belt Asteroids, as this is the source region of potentially hazardous near
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unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and distributed intelligence across the computing continuum. In
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, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance
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, acceleration processes, and interactions with Mercury's surface and exosphere. A key emphasis will be placed on analyzing the three-dimensional distributions of the ions observed by the Mass Spectrum Analyzer
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verticals, Unmanned Aerial Vehicles, Integrated Satellite-Space-Terrestrial Networks, Quantum Communications and Key Distribution, Spectrum Management and Coexistence, Tactile Internet, Earth Observation, and
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funded by EPSRC. The project will be led by Carmine Ventre, https://www.kcl.ac.uk/people/carmine-ventre . The role holder will join the Distributed AI group in the Department of Informatics at King’s
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algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project "Utility Optimization in Quantum Networks: Algorithm Design and Analysis", working with Dr
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algorithms for entanglement distribution in quantum networks. The purpose of the role is to contribute to the project “Utility Optimization in Quantum Networks: Algorithm Design and Analysis”, working with Dr
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of cyber-physical systems is available from March 15 th or as soon as possible thereafter. The employment will be for a period of 12-24 months. The candidate will be part of the section Distributed, Embedded