85 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions in Brazil
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knowledge and advanced transfer learning techniques. The methodology incorporates fundamental radar wave propagation equations into the diffusion process, allowing for more accurate and physically consistent
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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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and Supply (SAASP). Duration: 36 months (full-time) Expected start: April 2026 (flexible) How to apply Applications should be submitted only through the Google Form: https://docs.google.com/forms/d/e
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learning-based segmentation, multimodal image fusion, and radiomic feature extraction to construct clinically relevant prognostic models. Conducted at the Heart Institute (InCor) of Hospital das Clínicas
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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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in Amazonia and background on meteorology, chemistry, biology and environmental physics is desirable. For more details access the link with the full proposal: https://1drv.ms/b/c/87b35e20128a7567
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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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http://www.fapesp.br/oportunidades/9131 Requirements Additional Information Eligibility criteria Eligible destination country/ies for fellows: Brazil Eligibility of fellows: country/ies of residence: All