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data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
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sequential experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect heterogeneity statistical
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sequential experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect heterogeneity
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trial design, and sequential experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect
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conducting field work under remote or logistically challenging conditions - Experience with advanced quantitative methods, such as causal inference, Bayesian statistics, hierarchical models or spatial
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
(e.g., DeepONet, Fourier Neural Operators) Knowledge of uncertainty quantification, inverse problems, Bayesian inference, or parameter identification In addition you have: A high degree of initiative and
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project, including the host department, PhD programme, starting date, and project-specific qualification requirements, is provided below and in the links. Project descriptions Project 1: Predictive Bayesian