50 machine-learning-"https:" "https:" "https:" "https:" Fellowship positions in Portugal
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of the state of the art in machine learning for generation of artificial data; - identify and select the appropriate methods for the study in question; - develop the research capacity through the application
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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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Collaborate in the use of remote sensing and machine learning methods to detect A. longifolia and to monitor the spread and effects of the biological control agent (occasional collaboration). Activity 4
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Pose EstimationStrong background in computer vision and machine learning applied to pose estimation and visual servoing; Experience with OpenCV, PCL (Point Cloud Library), PyTorch/TensorFlow, and 3D
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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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|2025/795 under the scope of the Project Machine Unlearning in Speech Foundation Models: Learning to Forget (LeaF), Refª 2024.14611.CMU , funded Fundação para a Ciência e a Tecnologia, I.P., is now