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://www.di.ubi.pt ), under the following conditions: Research Field: Machine Learning/Pattern Recognition Objectives: Unsupervised Incremental Representation Learning for Ground-Based Drones in Dynamic Industrial
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://www.di.ubi.pt ), under the following conditions: Research Field: Machine Learning/Pattern Recognition Objectives: Foundational Models for Human-Machine Interaction Work plan: The work consists in develop a system
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-oriented GridFM learning tasks, including power-balance and voltage-related objectives, towards the prediction of voltages, branch flows/loadings, distribution factors and technical violations. Validate
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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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requirements: Candidates must hold, at the time of application, a Bachelor’s degree in Informatics Engineering or related fields. Candidates must also have knowledge in: i. Deep Learning and LLMs: practical
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supporting documentation, proven experience in all of the following areas: Computer vision and video processing (ingestion, ROI, 2D/3D keypoints, heatmaps); Deep learning and temporal modelling (CNNs
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the European Union through the COMPETE 2030 Programme, of Portugal 2030, under the following conditions: Scientific Area: Machine Learning Admission requirements: Candidates who cumulatively meet the following two
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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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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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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