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., stochastic gradient methods and Bayesian learning), Probabilistic performance guarantees, leveraging tools from stochastic systems, RKHS-based learning, and Bayesian inference to certify performance and
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within the SecReSy4You MSCA Doctoral Network at Eindhoven University of Technology. Information The Dynamics and Control group at Eindhoven University of Technology (TU/e) conducts world-class research
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networks, Bayesian neural networks, conformal prediction intervals and generative AI for synthetic data generation. You will also develop frameworks for uncertainty quantification in forecasting and
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undergraduate and graduate courses and workshops, thesis supervision, and curriculum development. Your qualities Above all, we are looking for a proactive team player with outstanding communication and networking
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generative models, methods for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. Want
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, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
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communication and networking skills. The ideal candidate is a dynamic and independent researcher who works effectively in interdisciplinary teams and is adaptable to diverse ways of working and doing science
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
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), and physiological parameters in the study of animal behaviour; a strong background in data analysis using R, preferably experience with Bayesian statistics and social network analysis; lab experience
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within brain networks. Among several proposed mechanistic accounts, the Bayesian predictive coding framework has gained increasing prominence. According to this framework, perception of proprioceptive