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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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frameworks. Course work such as in causal inference or implementation science will be encouraged. Basic Qualifications Applicants should have a PhD or equivalent in epidemiology, economics, public health
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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
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Science, Computer Science, Applied Mathematics, Engineering and Physics. Additional Qualifications Expertise (or desire to work) in reduced order modeling, Causal inference and High Performance Computing
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and extend skills in developing study protocols, drafting proposals, designing research instruments, creating analytical frameworks. Course work such as in causal inference or implementation science
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research associates to develop and apply theoretical and computational approaches to study complex cell biological systems. The emphasis will be on combining statistical inferences and biophysical modeling
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frameworks. Course work such as in causal inference or implementation science will be encouraged. Basic Qualifications Applicants should have a PhD or equivalent in epidemiology, economics, public health