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of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as well as the preparation of presentations, scientific publications, and technical reports
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(pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models; integration and analysis
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factors: Prior experience in developing algorithms for biomedical image processing (especially aligned with the research group's areas) and machine learning/deep learning techniques. Prior knowledge of data
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physiological signals, with a focus on ECG, and to develop machine learning and deep learning methods for classifying clinical, health, and wellness findings. Supporting project management and research group
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TEC. 2. OBJECTIVES: Development of evaluation methodologies in federated learning and new federated learning techniques applied to underwater oceanic environments for processing tabular data, images
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machine-learning methods for sample segmentation and classification. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: The fellow will join the INESC TEC team within the LIBScan project, carrying
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requirements to be integrated into the UAV under development.; ; - Development of Deep Learning algorithms for processing information from visible spectrum cameras, thermographic cameras, and LiDAR systems
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learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge
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of the Grant are:; 1) To apply machine learning algorithms for the diagnosis of faults and malfunctions in photovoltaic plants, using data from SCADA systems combined with synthetic data from digital twins (DT
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Analysis and Decision Support - Applying statistical and machine learning methods to interpret the data, identify trends for optimizing aquaculture conditions.; • Experimental Validation - Conducting