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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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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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