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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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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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. 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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of the regional datasets for the project data and knowledge base; • Create analytical workflows; • Lead scientific publications; • Contribute to developing / teaching relevant course material. For a full
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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
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Python and R; - Demonstrable experience with Machine Learning; - Excellent problem-solving skills and the ability to work both independently and as part of a team. This position is for full-time, on-site