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position within a Research Infrastructure? No Offer Description Activities The fellow will be expected to research the relationship between these technologies (big data, machine learning, and the entire
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involve developing an approach that uses Knowledge Organization (KO) metadata and ontologies to optimize parallel processing and scheduling policies (via Kubernetes) for Machine Learning tasks. The fellow
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. Familiarity with frameworks such as TensorFlow and Keras, as well as libraries including Scikit-learn, NumPy, and pandas; - Experience with machine learning models such as Extreme Learning Machine (ELM
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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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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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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
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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly