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of algorithms for analyzing physiological and biomechanical signals acquired by devices with integrated sensors, which will perform electrocardiography, electromyography and movement measurements, among others
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/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: Real-time signal analysis algorithms, feature identification, and personalized
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Engineering or related areas. Experience in research activities related to electrochemistry, biosensors, namely, construction of DNA-based sensors and paper-based electrodes. In case the Master degree
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and optimisation algorithms, focusing on their practical application in the context of the RaceEngineerAI project. Tasks include: - Developing models capable of simulating the behaviour of racing
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cloth-based sensors for pharmaceutical pollutants: Novel green tools to guarantee fish safety and surveil ecosystems”, reference 2022.07089.PTDC, funded by Fundação para a Ciência e a Tecnologia, I.P
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
for Science and Technology, I.P., both in their current wording. 2. Activity objective: Research and implementation of advanced Optimization and decision-support algorithms using Artificial Intelligence applied
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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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recommendation mechanisms based on semantic analysis and natural language processing, with the aim of facilitating collaboration and convergence of proposals. Developing and training NLP algorithms in multiple
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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