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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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validate the predictions of the ML models by means of atomistic modeling, in particular density functional theory (DFT) calculations, obtaining simulated electronic and emission spectra for the CDs. Finally
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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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