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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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: This project is related to machine learning for Urban Informatics. In this context, the intersection between the urban infrastructure and digital technologies plays an essential role. The aim is to develop
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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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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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). The position offers the opportunity to interact with international collaborators and industry partners. The fellow is expected to explore and design solutions based on machine learning (ML), as well as the use