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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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will focus on integrating image processing, machine learning, and deep learning techniques to improve the characterization and modeling of reservoir rocks. In the first stage, CT, NMR, and BHI images
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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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position within a Research Infrastructure? No Offer Description The research proposal aims to use machine learning, including large-scale language models, to analyze large datasets of smaller Solar System
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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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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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» OtherEducation LevelPhD or equivalent Skills/Qualifications Field of Knowledge: Computer Science / Geology / Geophysics / Electrical Engineering / Mathematics - Field of Activity: Machine Learning Additional
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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