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in mechatronic hardware and software You have a solid foundation in probability and statistics for Bayesian modelling, uncertainty quantification, and causal inference You have a team player mindset, a
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or more of the following: ecological modelling, dynamical systems, network analysis, Bayesian statistics or probabilistic modelling, mathematical biology, multivariate data analysis. Interest in connecting
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in mechatronic hardware and software You have a solid foundation in probability and statistics for Bayesian modelling, uncertainty quantification, and causal inference You have a team player mindset, a
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deep experience with PyTorch, JAX, or TensorFlow. Broad knowledge of modern ML and optimization (gradient‑based, evolutionary, Bayesian, reinforcement learning). Hands‑on experience with generative
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study examining common elements in decisions across different contexts (risk, uncertainty, time; gains, losses, and mixed domain choices). Applying Bayesian techniques to develop stochastic models
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in mechatronic hardware and software You have a solid foundation in probability and statistics for Bayesian modelling, uncertainty quantification, and causal inference You have a team player mindset, a