22 machine-learning "https:" "https:" "https:" "https:" "https:" scholarships at INESC TEC
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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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to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0554.pdf CALL FOR GRANT APPLICATIONS (AE2025-0554) INESC
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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
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programme Reference Number AE2025-0509 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2025-0509
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tools, with particular focus on multi-threaded and distributed scenarios. Experience with observability tools, particularly OpenTelemetry. Solid knowledge and experience in machine learning, deep learning
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions