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/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: • modelling and optimisation of PCM thermal storage for buildings and industry use
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 30 days ago
, with application to biomedical flows (e.g. microcirculation) it is now intended to develop an improved algorithm for advanced particle tracking using machine learning. The algorithm should be tested in
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) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Applying anomaly detection algorithms for streaming network data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND
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to grant holders" (https://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - Study and improvement of developed
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to grant holders" (https://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - broaden the knowledge of the state
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. OBJECTIVES: - To expand knowledge of the state of the art in the area of radar + video systems for motorcycles; - Identification and selection of the most adequate optimization methods to address the proposed
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insurance, supported by INESC TEC. 2. OBJECTIVES: The main goal of this grant is to improve the performance of key-value storage systems, such as RocksDB, while ensuring strict durability guarantees
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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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to grant holders" (https://www.inesctec.pt/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - broaden the knowledge of the state
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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