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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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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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qualitative nature of the methodology to be developed, which requires dialogue between the researcher and the research subject. To learn more about the proposal, request further information by email at
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learning and community engagement in conservation. Requirements: PhD completed; fluency in English; experience with qualitative methods; experience with and availability for fieldwork, in accordance with
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WaterWeave project, which focuses on innovative solutions for monitoring and the sustainable management of water resources. The fellow will develop machine learning and cloud computing techniques to estimate
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machine learning (ML) algorithms to identify previously unknown correlations between synthesis parameters (inputs) and optical, electronic and chemical properties (outputs), such as quantum yield, light