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-performance scientific computing, integrating traditional physics-based approaches with data-driven methodologies, including machine learning, deep learning, data mining, neural networks, and artificial
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physics-based approaches with data-driven methodologies, including machine learning, deep learning, data mining, neural networks, and artificial intelligence. The position emphasizes iterative methods
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development for innovative exploration of brain signals - Machine learning applications to multimodal brain and speech signals - Investigation of sensorimotor functions in animal models - Cortical recordings in
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and software development for innovative exploration of brain signals - Machine learning applications to multimodal brain and speech signals - Investigation of sensorimotor functions in animal models
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of numerical methods and software for high-performance scientific computing, integrating traditional physics-based approaches with data-driven methodologies, including machine learning, deep learning, data
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machine learning Where to apply Website https://pica.cineca.it/units Requirements Additional Information Work Location(s) Number of offers available1Company/InstituteUniversità degli Studi di
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, an alternative analysis model will be designed that, starting from the synthetic data generated by GANs, can improve the supervised learning of further machine learning algorithms and optimize I/O on large amounts
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details useful for system analysis. Next, a machine learning algorithm will be developed to classify crop types, trained on both real satellite images and synthetic images generated by GANs. Finally, a
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specific courses in Computer Engineering (SSD IINF-05/A). The winning candidate will be involved in research in the field of artificial intelligence and deep learning applied to images and videos, using
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language required: excellent level of English; Knowledge of Italian: level adequate to carry out the assigned teaching load (the researcher is required to teach in Italian); Maximum number of publications