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thermal imaging data, and potential clinical and signal data, to create algorithms capable of recognizing key clinical activities and interventions. Building on recent advances in computer vision and
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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of structures, facilitating a form-finding process driven by FEM analysis. Training deep learning algorithms to suggest multiple structural concepts tailored to specific boundary conditions. Expanding FEM
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