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, ● Methods for heterogeneous treatment effects estimation, ● Methods for multiple exposures, multiple outcomes, ● ML and AI methods for causal inference, ● Bayesian causal inference, ● methods
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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team
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on type, size, location of data centers in the US, their electricity and water demand, carbon emissions; exposure to air pollution. ● Develop and/or apply methods for causal inference and machine learning
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(https://www.hsph.harvard.edu/lin-lab/ ), Professor of Biostatistics and Professor of Statistics. The postdoctoral fellow will develop and apply statistical, machine learning (ML), and AI methods