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researcher in algorithmic game theory and/or online learning, working with Prof. Celli at BIDSA and the Department of Computing Sciences. The project studies how multiple machine learning algorithms interact
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algorithm development to hardware in the loop (HIL) validation, architectures for autonomous coordination through guidance, navigation and control of distributed space segments, under cooperative and non
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network aims to deliver fiber-optic quality experiences over wireless links by building the theoretical, algorithmic, and architectural foundations of THz systems. It introduces ultra-MIMO (multiple-input
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methodologies for measuring social and environmental impact. The main activities will include analyzing available impact data sources, designing models for impact assessment, and developing algorithms
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networks. The activities will include the development of algorithms for processing seismic and geodetic signals, subsurface imaging, dynamic characterization of sites and structures, as well as monitoring
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. The candidate will work on wind turbine and wind farm control, developing innovative control algorithms and validating them through wind tunnel testing. The candidate will also be responsible for teaching
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a big plus: Relevant publications (and/or M.Sc. thesis) on the above-mentioned research topics Programming Microcontrollers and Interfacing Sensors Machine Learning Algorithms and Deep Neural Networks
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, the concept of chirality and its spectroscopic consequences ii) gain experience in the development and management of commercial software iii) S)he will be trained in the most promising theoretical approaches
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at the Edge (CAITE) whose aim is twofold. First, it aims to bring AI at the edge, by providing scalability and resource efficiency through the development of cooperative, distributed AI algorithms, optimising
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, the selected researchers will deal with: Research & Development: Designing, developing, and implementing state-of-the-art machine vision and deep learning algorithms to analyze complex image and sensor data