58 machine-learning "https:" "https:" "https:" "https:" "RAEGE Az" Fellowship scholarships at INESC TEC
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domain in the design of deep learning algorithms for cardiovascular disease detection. 4. REQUIRED PROFILE: Admission requirements: Master’s degree in Biomedical Engineering, Computer Engineering
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PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop
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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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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
systems; - experience in applying Artificial Intelligence/Machine Learning and/or optimization algorithms to wireless networking systems.; Minimum requirements: The four Research Initiation Grants to be
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PROGRAMME AND TRAINING: - extend the knowledge of the state of the art in machine learning for lung cancer imaging data; - identify and select the appropriate methods for the study in question; - develop
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Website https://www.inesctec.pt/en/opportunity/AE2025-0532 Requirements Specific Requirements Academic qualifications: Training in Electrical and Computer Engineering. Minimum profile: • Be enrolled in a
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programme Reference Number AE2025-0527 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0527
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Framework Programme? Not funded by a EU programme Reference Number AE2025-0599 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https
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Programme? Not funded by a EU programme Reference Number AE2025-0601 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https
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on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: Experience in musical audio machine learning frameworks, advanced knowledge in music theory, and