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water quality parameters and predict cyanobacteria blooms in the Tietê system reservoirs. Activities: 1. Develop machine learning models for estimating water quality parameters via remote sensing; 2
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or abroad, in Soil Science, Remote Sensing, Environmental Sciences, or Pedometrics; - Proven experience in pedometrics, soil remote sensing, predictive soil modeling, soil organic carbon, and data science
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and radar remote sensing, climate time series, and hydrological models. The work will employ machine learning and explainable AI techniques to improve flood prediction under different hydroclimatic
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University of Campinas (UNICAMP) in Brazil, have an open call for 2 post-doctoral fellowships for the project “Hybrid hyperspectral-SAR remote sensing for agriculture application”, an international cooperation