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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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analyzed with various Machine Learning and Data Science techniques to assess the dynamics involving case processing and costs. The first phase of the research will organize general data from the lawsuits
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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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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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(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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models of therapeutic response using machine learning combined with Fourier-Transform Infrared Spectroscopy (FTIR) applied to blood, saliva, and tumor tissue samples. Requirements: PhD completed by
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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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experience in stellar astrophysics, survey data analysis, or machine learning, and strong programming skills. The position is for 12 months (renewable), based at IAG-USP. The fellowship is R$ 12,570/month
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Areas (Codes 25–29) 1. Machine Learning (Code 25) Objectives: Support UFABC’s undergraduate and graduate programs, strengthen research in Machine Learning, and expand English-taught course offerings