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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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to investigate the role of Annexin A1 (AnxA1) in innate lymphoid cells (ILCs) during the development of inflammatory bowel disease. The post-doctoral researcher will perform experimental and analytical activities
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the state of São Paulo (Brazil), using Light Detection and Range-LiDAR profiling data covering the entire state. LiDAR technology will enable a detailed analysis of forest structure, while deep learning
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techniques for the development of wearable devices capable of detecting plant health markers and analytes of agricultural interest. Candidates must hold a PhD degree in Chemistry, Materials Science, or related
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vesicles (OMVs), exploring OMV-based cancer nanovaccines by using analytical approaches, cell culture systems, and animal models. The fellow will be based at CNPEM's Brazilian Biosciences National Laboratory
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governance strategies. The study will combine document analysis, interviews with public managers and international experts, and the construction of a comparative analytical framework covering health policies
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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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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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. 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