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to predicting high utilization of healthcare services, using machine learning techniques applied to clinical data. The plan is focused on learning and skill development, with potential to contribute
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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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: Machine Learning/Pattern Recognition 2. Objectives: Mitigating Negative Transfer in Incremental Task Learning for Industrial Ground-Based Drones. 3. Requirements for admission and hiring: - Hold a BSc
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: Research Field: Machine Learning/Pattern Recognition Objectives: AI-based module for energy consumption and quality control optimization. Work plan: The planned work involves collaboration in the process of
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: Research Field: Machine Learning/Pattern Recognition Objectives: AI-based module for energy consumption and quality control optimization. Work plan: The planned work involves collaboration in the process of
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RE-C05-i08 do Programa de Recuperação e Resiliência, através da Fundação para a Ciência e a Tecnologia - FCT, nas seguintes condições: Scientific Area: Computer Engineering, Biomedical Engineering
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: Machine Learning/Pattern Recognition 2. Objectives: Unsupervised Incremental Representation Learning for Ground-Based Drones in Dynamic Industrial Environments 3. Requirements for admission and hiring
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: Machine Learning/Pattern Recognition 2. Objectives: Lifelong Incremental Learning for Navigation in Dynamic Industrial Environments 3. Requirements for admission and hiring: - Hold a BSc degree in
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; b. Indexed publications in the field of this call. b) Preferential factors: a. Knowledge in Artificial Intelligence and Machine Learning. Workplan and objectives to be achieved: The BIPD
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to facilitate the integration of the framework with external systems and educational platforms; Establish a Machine Learning Operations (MLOps) pipeline to automate the lifecycle of models, including training