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process and the results obtained. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - To contribute to the specification and development of algorithms for optimizing energy systems with
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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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visualisation (libraries such as Three.js, OpenGL, VTK, or similar); - Advanced knowledge of optimisation algorithms; - Previous experience with software development for logistics problems; - In-depth experience
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algorithms to combine information on cardiovascular activity obtained from heart sound signals, electrocardiogram, and photoplethysmography. Investigate the inclusion of prior knowledge about the application
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distributed systems. Minimum requirements: - Solid knowledge of database engine implementation; - Solid knowledge of optimization algorithms (Volcano/Cascades); - Practical development experience with
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manipulators capable of adjusting their trajectory and resistance in real time in response to variable external loads. This module should integrate learning algorithms based on artificial intelligence, allowing
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algorithms; Minimum requirements: - experience with cross-platform mobile development frameworks (Ionic); - experience in software development using the Python programming language. 5. EVALUATION
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-oriented interfaces is desired.; In parallel, we intend to explore new optimizations, such as data deduplication, support for multi tenancy, and new scheduling algorithms. These optimizations should be
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://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - Development and testing of algorithms and methodologies based
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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