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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
of this project requires the design, development, and training of an artificial intelligence algorithm capable of automatically segmenting the bony structures of both healthy and fractured tibial plateaus
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with disease and/or drug experts (clinicians, clinician scientists, data scientists, and laboratory investigators) to co-develop phenotyping algorithms but is expected to serve as the domain expert in
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the technical side, we aim at combining statistical latent variable models with deep learning algorithms to justify existing results and allow a better understanding of their performances and their limitations
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research work will be to devise efficient algorithms for source separation in DAS measurements. Issues such as large data volumes that can exceed 1 To per day and per fiber, instrument noise, complex nature
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drug experts (clinicians, clinician scientists, data scientists, and laboratory investigators) to co-develop phenotyping algorithms but is expected to serve as the domain expert in high-throughput
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preferred. Experience utilizing computer vision algorithms and frameworks, including medical image classification, feature matching, edge detection, image segmentation, and deep learning models like