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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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these images. This project proposes an innovative approach that combines state-of-the-art diffusion models with physical radar knowledge and advanced transfer learning techniques. The methodology incorporates
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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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curiosity combined with a focused and independent research profile; - Solid background in at least three of the following areas: enzymology, biocatalysis, natural products, analytical chemistry, and
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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
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learning-based segmentation, multimodal image fusion, and radiomic feature extraction to construct clinically relevant prognostic models. Conducted at the Heart Institute (InCor) of Hospital das Clínicas