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, including electrocardiograms and other wearable sensors, for subsequent application of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as
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sensors, for subsequent application of machine learning and deep learning methods and classification of health and wellness parameters. Data acquisition, as well as the preparation of presentations
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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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). Development and validation of machine learning models for calculating occupational health indicators. Integration, management, and analysis of data from wearable monitoring devices. Experimental evaluation
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; - Development of a simulation module for parcel delivery operations, integrating traditional simulation techniques with Machine Learning models; - Development of algorithms for the integration of Machine Learning
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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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15 Jul 2026 Job Information Organisation/Company INESC TEC Research Field Computer science Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Application Deadline 29
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prototypes, transforming them into useful information to support agronomic decision-making; Apply data processing and machine learning techniques to relevant problems in an agricultural context; Support the
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developing solutions for locating and manipulating semi-rigid objects or complex geometry. Investigating machine learning strategies with limited data, including the generation of synthetic data in simulation