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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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Together, these research directions seek to reimagine how buildings and cities operate—optimizing energy use, enhancing human well-being, and reducing carbon emissions at scale. We are seeking multiple
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discussions and present results to Dr. Regehr and other members of the lab on a regular basis. Additionally, postdocs will prepare presentations or posters to discuss results in department-wide seminars and at
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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the range of $67,600 per year, depending on whether a recently defended graduate or already with 1 year of postdoc experience in the Ph.D. lab. The position offers full Harvard benefits. Basic Qualifications
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appointment). Strong background in statistical or machine learning methodology, optimization, or high-dimensional data analysis. Proficiency in R or Python; experience with deep learning, causal inference
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of optimizing pipelines for large-scale genomic projects. Special Instructions Required documents: CV Research summary of PhD work. Cover letter describing your interest in the lab and initial ideas for new
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design and execute experiments using lung cancer and lung infection Organ Chips. Develop, optimize, and characterize human lung microphysiological models for translational studies. Analyze and interpret