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fluid mechanics and turbulent flows, high-performance computing, machine learning methods in computational problems. GSSI is a world-renowned research institute and school of advanced studies
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and image generation based on deep learning. The aim is to study techniques for handling multimodal data by integrating visual information (2D and 3D) with textual or tabular metadata. This integration
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materials according to the Lambert–Beer law, thus enabling an accurate description of PEC device behavior. In parallel, the coupling between kMC and CFD simulations will be achieved through machine learning
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, early detection of degradation, and residual life prediction. The program integrates physical modeling, machine learning, and data fusion techniques to optimize predictive maintenance, reduce operating
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include: (1) implementing light–matter interaction in CFD via the radiation transport equation and suitable attenuation models; (2) integrating kMC-based surface kinetics through machine-learning surrogate
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computational resources to ensure the efficiency of analysis, modeling, and machine learning tasks. The researcher also contributes to defining policies that ensure security, service continuity, and scalability
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experience in Artificial Intelligence (AI) and Machine Learning (ML) concepts, algorithms, and frameworks; Hands-on experience with popular ML libraries and tools (e.g., TensorFlow, PyTorch, scikit-learn
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the foundations of machine learning. He/She will have to be able to teach in Italian and/or in English. Teaching, supplementary teaching and student service activities will consist of 350 hours per year, of which
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Deep Learning Computer Vision Edge AI, TinyML, and Embedded AI Explainable AI Safe AI Federated, Parallel & Distributed, Computing/Learning Control Systems Optimization Planning and Scheduling Human
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and development of perception stacks for autonomous mobile systems in general in any field Machine learning/deep learning experience applied to perception and any experience with deep Learning