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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 2 months ago
1801P.01460.1.06 LARSYS/ISR BASE 2025-2029 - LASEEB LAB/ISR, financed by national funds through FCT/MCTES (PIDDAC Workplan: The scholarship holder will acquire simultaneous EEG/fMRI imaging from healthy
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Machine Learning components of the CONVERGE project toolset.; - Assist in executing integration tests across different hardware and software modules.; - Contribute to the structured collection and
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of Artificial Intelligence and experience using machine learning and deep learning development environments and libraries is a plus. As set forth FCT Research Scholarship Regulation No. 950/2019 of December 16
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the field of Artificial Intelligence and experience using machine learning and deep learning development environments and libraries is a plus. As set forth FCT Research Scholarship Regulation No. 950/2019
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Engineering/ Electrical Engineering. 2. Admission Requirements: Bachelor's degree in Computer Engineering, Systems and Information Technologies Engineering, Electrical and Computer Science Engineering, or in a
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) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Development of novel Machine Learning techniques applied in systems/networks research, which includes, but is not
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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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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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an emphasis on the development of methodologies and techniques for Evolutionary Computation and Machine Learning. Work plan: Review of the state of the art in Machine Learning and Deep Reinforcement Learning