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and implement Bayesian graph neural networks and convolutional neural networks as surrogates for high-fidelity biomechanical models Quantify and propagate uncertainty, and develop strategies for model
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theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers the opportunity to work with
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single cells make decisions during differentiation, in particular during development. Building on Bonsai, a Bayesian framework that leverages tree structures for distortion-free exploratory analysis
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