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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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data (PET, CT, Magnetic Resonance Imaging with Late Gadolinium Enhancement – MRI-LGE) and clinical variables. The approach encompasses unsupervised multimodal registration, three-dimensional deep
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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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). Candidates must meet the following requirements: - PhD in Computer Science; - Experience in research on the use of AI to recommend code refactoring opportunities; - Experience with data analysis and data
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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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met. Certified applicants will be notified later on of the dates when the selection process will take place. Please note that if an applicant’s PhD degree was granted by a non-Brazilian institution, an
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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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essential for patient care and direct communication. Requirements: - Bachelor's degree in a Life Sciences field and PhD defended within the last seven years in Molecular Biology or Medical Physiopathology
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. The University of São Paulo's School of Arts, Sciences and Humanities (EACH-USP) is CIATec's host institution. Requirements: PhD completed less than 7 years ago in Education or related areas; experience in