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, and citizenship); - Experience in building and organizing databases using international, national, and local data sources; - Experience in quantitative data analysis; - Work schedule and location: a
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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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on CV evaluation, will be contacted by email by April 13, 2026, to schedule an online interview. The position is open to Brazilian and international applicants. Proficiency in English and knowledge
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advanced compilation techniques for scientific and AI applications on heterogeneous GPU clusters. Research topics include scheduling, memory management, communication–computation overlap, and performance
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potential use of the Tietê–Paraná Waterway. 3) Solving problems associated with the definition of regular navigation services. 4) Modeling and solving the lock transit scheduling problem. Requirements: • PhD