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-AI learning - Develop methodologies to assess the robustness and safety of human decisions assisted by AI, hybrid co-learning between AI and humans, and fully autonomous AI, considering risk assessment
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-AI learning ; - Develop methodologies to assess the robustness and safety of human decisions assisted by AI, hybrid co-learning between AI and humans, and fully autonomous AI, considering risk
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.; - Develop skills in artificial intelligence and machine learning techniques for analyzing operational data and detecting anomalies, using foundational model approaches (e.g., GridFM project, LF Energy
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-award courses of Higher Education Institutions. Preference factors: Machine Learning Knowledge. Knowledge of signal processing and machine learning libraries (e.g., PyCaret, scikit-learn). Minimum
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TEC. 2. OBJECTIVES: Collaborate with clinical partners in data collection and annotation Design and implement new deep learning solutions for the analysis of heart sound auscultation, electrocardiogram
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devices. Minimum requirements: Advanced knowledge of machine learning models and Python tools for signal processing and machine learning. General knowledge of system architecture and APIs. Previous
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results. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - Develop machine learning-based models from data.; - Validate the developed models with real data.; - Publicize the work in international
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integrating third-party data (from connected data spaces or other federated digital learning platforms).; 3) Validate the developed methodologies on real data and real demonstration sites, in particular
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of Machine Learning techniques. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC), based
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AND TRAINING: - survey and analyze the state of the art in emerging wireless networks, including simulation aspects using real data assimilation, Machine Learning, and digital twin approaches