14 machine-learning "https:" "https:" "https:" "https:" "https:" Fellowship scholarships
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power systems, network analysis and power flow; - Experience or academic background in machine learning, Graph Neural Networks (GNN)/Grid Foundation Models and/or probabilistic methods and Monte Carlo
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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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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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planning and identification of strings and modules in the field. Development of a computer vision and machine learning pipeline for the detection, localisation and classification of defects in photovoltaic
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Reference Number AE2026-0230 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0230.pdf CALL FOR
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.; This solution will be fundamental to ensuring greater robustness and reliability of data-centric applications (for example, databases and machine learning tools), as well as the durability of the data processed
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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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insurance, supported by INESC TEC. 2. OBJECTIVES: • Explore machine learning approaches for discovering interpretable and clinically relevant visual representations.; • Validate the proposed methodologies
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. - Criterion 2: Knowledge in the scientific areas of the project: Academic or applied knowledge in Software Engineering, Intelligent Systems/Machine Learning, and Interactive Technologies. - Criterion 3