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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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, aiming to expand research fields. It includes the theoretical systematization of database aesthetics and the development of case studies to propose solutions that optimize metadata use. Activities involve
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in engineering or a field related to the project; • Knowledge and proven expertise in computational optimization methods and discrete-event simulation, with experience in regular service design
Searches related to knowledge optimization
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