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transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches to high-dimensional and multi-modal biomedical data. Causal Inference, Fairness, and Trustworthy AI
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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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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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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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computational research skills in one of the following areas: Economics: PhD in Economics or Strategy with expertise in causal inference, econometrics, experimental design, industrial organization, or applied
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as early as possible. Coordinate salesforce campaigns, send out invitations, and track RSVPs. Manage any travel associated with fellow events.Work with Harvard travel to book flights and hotels
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to the volume of applications we receive, we do not have time to track down missing letters. You may use Interfolio. Interfolio provides you with a unique email address for each letter of recommendation. You need