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WaterWeave project, which focuses on innovative solutions for monitoring and the sustainable management of water resources. The fellow will develop machine learning and cloud computing techniques to estimate
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and computer programming skills. The objective of this research is failure and damage detection in the petroleum artificial lift process using the operational data signal analysis (time series analysis
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solutions to hyperbolic equations, or more in general, for evolution equations; dispersive estimates; global existence (in time) of solutions to semilinear problems, possibly assuming small initial data
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postdoctoral fellowships. 1. Robust Methods for Volatility Estimation with High-Dimensional Data — design, implementation, and computational analysis. Supervisor: Prof. Luiz Koodi Hotta. 2. Forecasting Methods
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of other emerging strategies and techniques in AI, to detect synthetic realities and sources of misinformation. Applications and submission of documents via form: https://forms.gle/biBrEfns9oMAdp9X8
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(conferences) * Moving assistance (including flight tickets to São Paulo) * More information on FAPESP: http://www.fapesp.br/en/5427 ** About USP: The University of São Paulo, a prestigious institution and top