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24 Jan 2026 Job Information Organisation/Company FAPESP - São Paulo Research Foundation Research Field Computer science Researcher Profile Established Researcher (R3) Application Deadline 12 Feb 2026 - 23:59 (UTC) Country Brazil Type of Contract To be defined Job Status Not Applicable Is the job...
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Experience in machine learning, math, and programming. LanguagesENGLISHLevelGood Additional Information Work Location(s) Number of offers available1Company/InstituteUniverCountryBrazilState
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Science, Computer Engineering, or a related field, completed before the fellowship start date. Candidates should demonstrate experience in at least two of the following areas: compilers, parallel programming
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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Python and R; - Demonstrable experience with Machine Learning; - Excellent problem-solving skills and the ability to work both independently and as part of a team. This position is for full-time, on-site
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: Artificial intelligence applied to seismics, neural networks, machine learning, synthetic data generation, seismic inversion, geological CO2 storage. Abstract: This research project aims to develop a synthetic
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Foundation (FAPESP), and combines statistical analysis, spatial methods, and qualitative research. Georeferenced data from the Military Police and the Municipal Secretariat of Urban Security will be used
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preparation for use in AI models; - Experience with explainability techniques for Machine Learning models; - Desirable experience with system modernization. To apply, send an email with the subject “Inscrição
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the analysis and monitoring of detected events within the continuous monitoring of the SIN. The fellow will be based at the State University of Campinas's Faculty of Electrical and Computer Engineering (FEEC
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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