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-making methods powered by supervised and reinforcement learning, which aim at trustworthiness in AI-assisted human control with augmented cognition, hybrid human-AI co-learning and autonomous AI, with
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knowledge of machine learning models and Python tools for signal processing and machine learning. General knowledge of system architecture and APIs. Previous knowledge of physiological signal processing. 5
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biosignals. Application of machine learning techniques for classification of different classes using the extracted features. Assembly, documentation, testing, and use of an innovative biosensing system
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models to characterize lung cancer based on a non-invasive methodology. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning
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insurance, supported by INESC TEC. 2. OBJECTIVES: • Research novel deep learning models for anatomically structured EGGIM estimation.; • Develop methods for image-level and examination-level reliability
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applied to healthcare; • To explore approaches suitable for analysing incomplete and/or distributed data, using techniques such as imputation or federated learning; • To adopt good research and development
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.; This solution will be fundamental to ensuring greater robustness and reliability of data-centric applications (for example, databases and machine learning tools), as well as the durability of the data processed
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insurance, supported by INESC TEC. 2. OBJECTIVES: • Explore machine learning approaches for discovering interpretable and clinically relevant visual representations.; • Validate the proposed methodologies