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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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for treating bone fractures and osteoarthritis, as well as a digital twin of the OR. The laboratory's strategy is to create automated digital models, notably using machine learning and AI tools, for use in
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will focus on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural
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of new biomarkers as well as the formulation of an algorithm for determining the quality of grafts using machine learning. - Positioning in relation to the state of the art in the field and
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on low cost computer vision and machine learning, simulation assisted plant phenotyping and machine learning based data mining for plant biology. Description of the project My research mainly focuses