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computational models and data analysis code to process large, multimodal behavioral datasets using both traditional methods (e.g., factor analysis) as well as more modern approaches (e.g., deep learning
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natural and artificial intelligences process information. The project is led by Heiko Schütt and will employ one PostDoc and one PhD student. About the role... You will develop new Bayesian methods
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I-2503 – PHD IN EXPLAINABLE AI FOR DATA-DRIVEN PHYSIOLOGICAL AND BEHAVIORAL MODELLING OF CAR DRIVERS
(e.g., InfoGAN, β-TCVAE, TopDis / Topological Disentanglement, Independent Component Analysis (ICA), Variational Autoencoders (VAEs), and matrix factorization techniques); · Experience with
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