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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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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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. 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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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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optimization in distributed systems. The work also involves modern compiler infrastructures, with emphasis on MLIR, and contributions to LLVM and the OpenMP standard. Applicants must hold a PhD in Computer
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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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describing their progress and activities. Mandatory requirements Candidates must hold a PhD in Mathematics Education or a closely related field by the start of the fellowship. How to apply Applicants must
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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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level or within the Graduate Program. Mandatory requirements i) Applicants must have completed their PhD no more than seven (7) years prior to the application and must present an outstanding academic