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. Merits for this position: PhD acquired within three years of last application date. Documented pedagogical experience. Experience in image analysis and/or computer vision, especially in the context
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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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approaches for using machine learning to analyze X-ray data, particularly Resonant Inelastic X-ray Scattering (RIXS). The position will collaborate with experts in RIXS experiments (Mark Dean), computational
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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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. 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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, 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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language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative ways to understanding, processing, and
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and methods for advancing the research effort Design and carry out computer experiments on deep learning and related robotic simulations Collaborate with other engineers to create prototypes of embodied
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, biochemical, cell, and tissue biology method skills. Experience in using computational analysis (biostatistics, machine learning, data science, physics, or a related field). We value diversity and strongly
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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