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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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reendothelialization assays, platelet adhesion assays, and co-culture and immunomodulation studies. They should also be willing to learn new methods as needed, such as chemiluminescence-based nitric oxide (NO) detection
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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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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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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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of agronomic interest in sugarcane and forage species, such as resistance to biotic and abiotic stresses and vegetative vigor. Requirements: • PhD in one of the following areas: Genetics and Plant Breeding