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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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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
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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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(HR+/HER2-) and aims to develop predictive models of therapeutic response using machine learning combined with Fourier-Transform Infrared Spectroscopy (FTIR) applied to blood, saliva, and tumor tissue
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must hold a PhD in astronomy/astrophysics (awarded within the last 7 years), with experience in stellar astrophysics, survey data analysis, or machine learning, and strong programming skills
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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