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Transparency, and Societal Impact. Candidates should bring expertise in areas such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis
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analysis of models Alignment and language model agents Other backgrounds that could inform language model interpretability and control, such as cognitive science, neuroscience, causal inference
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such as: Causal inference and the design and analysis of experiments Reinforcement learning and sequential decision-making Analysis of complex systems, networks, and large-scale data Machine learning
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research tasks and projects, making use of selected methodologies (longitudinal designs, moderation and mediation, causal inference), library research (Pubmed searches, systemic review methods), and
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coursework in probability and statistical inference, a statistics-focused Master’s thesis, or relevant publications. Foreign completed degree (M.Sc.-level) corresponding to a minimum of four years in
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include: Item response theory, causal inference in non-experimental designs, psychometrics, randomized controlled field trials, longitudinal and multilevel modelling, machine learning methods, artificial
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cognitive experiments Expertise in both frequentist and Bayesian statistics performing descriptive and multivariate statistical analysis techniques such as SEM using SPSS and/or R language. Excellent ICT
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Learning: Experience with PyTorch, TensorFlow, or Hugging Face; embedding models; and model validation/deployment. Causal Inference/Experimentation: Knowledge of experimental design, randomization, and
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or Strategy with expertise in causal inference, econometrics, experimental design, industrial organization, or applied microeconomics. Experience with field experiments, quasi-experimental methods, and
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/deployment. Causal Inference/Experimentation: Knowledge of experimental design, randomization, and causal identification methods. There are no teaching requirements for these open positions. Basic