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of the art in the area of antenna characterization systems; - Identification and selection of the most adequate optimization methods to address the proposed workplan: ; - Develop the research skills through
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. OBJECTIVES: - To expand knowledge of the state of the art in the area of antenna characterization systems; - Identification and selection of the most adequate optimization methods to address the proposed
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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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of the art in the area of electronic devices for antenna applications ; - Identification and selection of the most adequate optimization methods to address the proposed workplan: ; - Develop the research
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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