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Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM) - Universidad Politécnica de Madrid (UPM) | Spain | about 2 months ago
information from aerial and ground-level sensors. • Knowledge of the processes of extracting data from sensors and analyzing the results. Specific Requirements • High level of English. • Graduate in
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for precision livestock farming within the Horizon Europe Re-Livestock project. The candidate will design and analyse mathematical algorithms to monitor and improve animal performance, welfare and environmental
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STUDENTS FUNDED BY THE UPV'S RESEARCH STRUCTURES – SUBPROGRAMME 2 (PAID-01-22) 119977 Development of mathematical and machine-learning algorithms to support an intelligent, integrated system for biosafety
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) Interpretable machine learning for network adaptation. In this thesis, the student will study how interpretable models and explainable learning algorithms could be used in real cellular networks for safe
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of federated algorithms that enable fair management of the advantages and duties of energy communities in which vulnerable people live. The automatic generation of data models using AI technology will enable
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. The successful candidate will work under the supervision of Profs. Boguñá and Serrano in the Mapping Complexity Group at the University of Barcelona. Link to the call for applications: https://seu.ub.edu
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implementation of security provisioning algorithm in DFL environment Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » Communication
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of the Basque Country (UPV/EHU) – POLYMAT, Donostia-San Sebastian, Spain • Supervisor: Prof. Haritz Sardon (UPV/EHU), Prof. Steven De Meester (Co-supervisor at Ghent University) • Duration: 36 months • Starting
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Framework Programme? European Union / Next Generation EU Reference Number 165526 Is the Job related to staff position within a Research Infrastructure? No Offer Description Analysis and design of algorithms
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generate, transmit, and detect OAM-entangled photons under realistic atmospheric turbulence. Deep learning algorithms will be employed to pre-compensate distortions in real time, maximizing state fidelity