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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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(longitudinal designs, moderation and mediation, causal inference), library research (Pubmed searches, systemic review methods), and statistical analysis (data visualization, descriptive analyses, time series
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postdoctoral research associate to work with Professor Michael Desai at Harvard University on projects involving inferring sequence-function landscapes, using a combination of empirical data and ML methods (e.g
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to high-dimensional and multi-modal biomedical data. Causal Inference, Fairness, and Trustworthy AI in real-world healthcare applications. Our group actively collaborates with large national and
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with leading machine learning frameworks and modern AI environments, including multi-GPU model training and large-scale inference on dozens to hundreds GPUs, are required. Additional Qualifications
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background. The ideal candidate will have existing expertise in several of the following areas, aligned with our research focus: 1) Causal inference, invariant learning and representation 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