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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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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
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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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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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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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(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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» 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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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