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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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optimization in distributed systems. The work also involves modern compiler infrastructures, with emphasis on MLIR, and contributions to LLVM and the OpenMP standard. Applicants must hold a PhD in Computer
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Educational background and field of knowledge: Agricultural Engineering/Agronomy or related fields, with a focus on Plant Pathology. Specific Requirements The candidate must hold a PhD degree with a thesis
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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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, Pharmacy, or related fields; • PhD in Medicine, Sciences, Psychobiology, or related areas; • Strong background in Physiology, Reproduction, and/or Microbiology; • Experience in clinical and preclinical
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/axonopathies.” Prerequisites: • PhD degree in Biological Sciences or Health Sciences; • Experience in techniques such as Histology, Molecular and Cell Biology, Immunohistochemistry, miRNA and RNAseq analysis