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of renewable energy sources, with a focus on wind and photovoltaic plants. Through the integration of meteorological observations, high-resolution numerical models and machine learning algorithms, highly
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based on the new data generated, incorporating key variables identified in (i), and use statistical and machine learning methodologies to ensure high predictive accuracy and robustness; iii) validation
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, Biological Anthropology, Artificial Intelligence, Neurophilosophy, Neuroaesthetics,etc.) ○ Brain disorders(Psychiatry, Neurology, Rehabilitation Medicine, etc.) ○ Brain Engineering(Brain-machine interface
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sustainability reporting). Particular areas of investigation will include the study of accounting fraud and the analysis of the potential offered by big data and machine learning techniques in the processing and
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, Computer Science, Machine Learning, Probability and Statistical Physics. The program also benefits from contributions of distinguished visiting professors who deliver short monographic courses. The PhD in Statistics
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heterogeneous data for machine learning”. Where to apply Website https://reclutamento.dsi.infn.it Requirements Research FieldPhysicsEducation LevelMaster Degree or equivalent Research FieldComputer
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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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important applications concerns the monitoring of land cover in relation to climate change. In this context, although artificial intelligence and machine learning techniques have been widely used
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secondments at world-class institutions. The proposed research will be on the intersection of the following topics: Energy Harvesting and Intermittent Computing Batteryless Networking Machine Learning
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them to launch a substantive research agenda—and is complemented by electives that may cover other areas of mathematics or, depending on their interests, economics, finance, statistics, or machine