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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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. https://pai.seas.harvard.edu The postdoc is intended for one year but we anticipate there will be funding to potentially extend it to a second year. The postdoc will receive a generous salary as
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populations and biobanks for risk prediction, genetic discovery, and genomic medicine. Federated and transfer learning for distributed and privacy-preserving data integration. AI and Deep learning approaches
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing
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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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Ph.D. in a relevant field by the time of appointment. The postdoc will be based at the Harvard University Herbaria (HUH) and will have access to all the resources available through the Department of OEB
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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associate to work with Professor Christopher Kuzawa at Harvard University. The postdoc will oversee analysis and write up of an extensive longitudinal dataset that has followed a large population in Cebu
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. As this work exists at the interface of biology, chemistry, and engineering, applicants with training and education in any of these areas, or related fields, will be considered. Postdocs in the lab
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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins