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-board Payload Signal and Data Processing algorithms and techniques for RF payloads and instruments in close collaboration with TEC-ED; and Time and frequency references, modelling, design tools
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Communication/Digital Media is required, and a strong background and experience working with artificial intelligence systems, machine learning algorithms, augmented/virtual reality technologies, robotics, and/or
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algorithmic agents, the research explores how automation, predictive modeling, and generative intelligence transform sensemaking, decision-making, and adaptive capabilities. The goal is to develop a framework
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concepts in information processing using machine learning algorithms LanguagesROMANIANLevelExcellent Research FieldComputer scienceYears of Research ExperienceNone Additional Information Selection process
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contexts, including the use of surrogate models. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional Information Eligibility criteria
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sources. Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements Additional Information Eligibility criteria Oral test aimed at ascertaining candidates
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data. The work includes also the implementation of calibration and control algorithms for automated configuration and stabilization of the operating point of active photonic components. Where to apply
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of signal processing and machine learning algorithms for the extraction of acoustic, prosodic, and semantic parameters from voice recordings. Alongside the innovative research activities, the project requires
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The project consists in the development algorithms for the indirect estimation
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 10 hours ago
of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model