29 algorithm-development-"Multiple"-"Prof"-"Prof"-"SUNY" scholarships at INESC TEC in Portugal
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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
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that provides AI-based suggestions. The work will consist in the improvement and evolution of previously developed models, as well as interacting with project partners to integrate algorithms and conduct field
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algorithms for analyzing electrocardiography, electromyography and movement signals, identifying characteristics and recognizing patterns in everyday activities. Testing and validation of methods developed in
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or international conference. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: Research and development of algorithms for analyzing signals acquired in real time by a system with integrated
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insurance, supported by INESC TEC. 2. OBJECTIVES: Study the state-of-the-art in space robotics, focusing on navigation, control, and gas propulsion systems.; • Develop navigation, detection, and localization
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17172 (COMPETE2030-FEDER-00864900) co-funded by the ERDF - European Regional Development Fund through Innovation and Digital Transition Program - COMPETE 2030 under the scope of Portugal 2030 and by
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OF THE WORK PROGRAMME AND TRAINING: 1) Development of workflows and algorithms to complement datasets of connected data spaces, to improve analysis results (forecasting, analysis of financial tools, predictive
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of the art in emerging wireless networks; - identify and select the methodologies and approaches most suitable for the development of the work; - strengthen the research and development competencies
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; - collaborate in the preparation of technical reports on the algorithms, mechanisms, models, or protocols developed; - develop new modules to enable the simulation and/or experimentation of emerging wireless
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of a multi-modal dataset.; - Implementation of a software module for storing datasets according to a pre-defined standard.; - Development of routines for testing existing ML algorithms on a multimodal