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. Create predictive algorithms for the occurrence of cyanobacteria blooms; 3. Support field activities, process large volumes of data, and contribute to scientific publications. Requirements: • PhD in areas
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Algorithms for Agro-industrial, Health, and Environmental Sustainability, within the FAPESP Center for Research and Innovation on Smart and Quantum Materials (CRISQuaM). The project aim to develop quantum
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machine learning (ML) algorithms to identify previously unknown correlations between synthesis parameters (inputs) and optical, electronic and chemical properties (outputs), such as quantum yield, light
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, and analysis by using concepts and methods from machine learning, including pattern recognition, graphs, and complex networks. ** Specific Research Areas: * Develop concepts and algorithms to analyze
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(“Autonomous System for Hybrid Hyperspectral-SAR Monitoring in Precision Agriculture”, Supervisor Prof. Hugo Hernández Figueroa, and “Development of Methodology and Robust Operational Algorithms for Hybrid