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The Section of Biostatistics is looking for a postdoc to develop statistical methods for inference on causal effects in studies affected by non-random participation, particularly self-selection in
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methods and their underlying assumptions and limitations. Demonstrated experience with statistical analysis of genomic and ecological data, including multivariate analyses, demographic inference, population
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carbon, nitrogen, and water flows in agroecosystems. A solid background in uncertainty quantification, applied statistics, Bayesian calibration, and Monte Carlo simulations. Strong skills in scientific
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and inference for distributed AI agents, and how the realities of networked operation shape the design of adaptive, resilient intelligence. Finally, the research will address how groups of embodied
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informative but also pose significant privacy risks. Your work will focus on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic
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inference. Both postdocs are expected to combine their respective methodological profiles with an active interest in computational tools. They will be part of a collaborative team including the PI and student
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inferred from necropsies; and breeding origin of the birds involved, determined through DNA-based assignment. Your main responsibility will be to make sense of the data, help understand the impact of
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proficiency in English (written and spoken). The applicant must have excellent skills in quantitative methods, including methods for causal inference and/or survey experimental designs. To fulfill the research