72 algorithm-development-"Multiple"-"Prof"-"Simons-Foundation"-"St" positions at INESC TEC in Portugal
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mathematics,Informatics Job summary: INESC TEC is accepting applications for 1 RESEARCHER job in the Software Engineering and API Development for Power Systems Project: Scientific Advisor: Tiago André Soares
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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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: • Experience in developing image processing algorithms for weeding or pollination; and; • Experience in developing micro drones with potential for weeding/pollination. Funding Entity: on the scope AGROBOOST
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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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initiatives, particularly in the field of Energy Systems - Energy Transition. The objectives are:; - Development and application of artificial intelligence algorithms for different use cases in the energy
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selection of sensors and their respective lighting to be adopted.; 3. Study and development of algorithms for detecting inconsistencies.; 4. Study and implementation of operator interfaces.; 5. Assembly and
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are to research and develop new features for the Intel SPDK platform (Storage Performance Development Kit). More specifically, we are looking for new scientific contributions that:; 1) improve the platform's
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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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://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