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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 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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(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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of machine learning tools. Fellowship Details: The fellowship is for 3 years, with possibility of extension. Please submit a cover letter, including experience and motivation, and your full CV to rvr
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the supervision of Prof. Dr. Marilene Proença Rebello de Souza, within the project Center for Science for the Development of Basic Education: Learning and School Coexistence (CCDEB) (FAPESP Process 2024/01122-7
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the supervision of Prof. Dr. Marilene Proença Rebello de Souza, within the project “Center for Science for the Development of Basic Education: Learning and School Coexistence (CCDEB)” (FAPESP Process 2024/01122-7
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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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staff position within a Research Infrastructure? No Offer Description The Center for Science for the Development of Basic Education: Learning and School Coexistence (CCDEB) announces a call for one
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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