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Wu Tseng. The BIG-CT research laboratory is focused on design and development of X-ray based imaging systems, imaging techniques, image processing, and image analysis, with an emphasis on clinical
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slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and demonstrated experience in computer vision or analysis of pathology images
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, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology. Achieving
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. SILEX 2025) to calculate the Fire Radiative Power (FRP) and compare with satellite observations (VIIRS, SLSTR, FCI). Develop a fire front segmentation algorithm using machine learning techniques (deep
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research protocols and procedures, including in computational tasks where data visualization, preprocessing, or interpretation can be improved. Devises and deploys custom machine learning approaches where
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. Evaluates machine learning training using tools such as precision-recall metrics, receiver-operating characteristics curves, and confusion matrices. Trains and evaluates neural networks for computer vision
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related field) with a specialization in image processing and machine learning. They should demonstrate strong algorithmic programming skills (in Python, and possibly C++) and be comfortable working with
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. Collaborate on multidisciplinary projects involving high-throughput phenotyping platforms. Apply machine learning and deep learning techniques to improve image processing and trait prediction. Analyze large
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) Theoretical knowledge and practical experience in artificial intelligence-driven techniques for image processing Excellent proficiency of oral and written English in a scientific context Meriting criteria
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, involving expertise in optics, electronics, image and data processing, chemistry, and biology. With the support of several European funding programs, the team is building a data science and machine learning