13 combinatorial-optimization Fellowship positions at Harvard University in postdoctoral
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Details Title Postdoctoral Fellow in Riemannian Optimization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description A postdoctoral position is
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candidate will participate in a NIH grant-funded project on optimism and longevity. The research involves conducting both primary data collection exploring new methods for measuring optimism (or other
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candidate will participate in a NIH grant-funded project on optimism and longevity. The research involves conducting both primary data collection exploring new methods for measuring optimism (or other
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transcriptomics datasets, and of paired molecular and functional measurements (e.g., electrophysiology), as well as optimally leveraging integration with existing genomics datasets. The role is focused
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datasets as well as optimally leveraging integration with existing genomics datasets. The role will often involve rapid prototyping in support of a dynamic, fast-moving experimental program; it is focused
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establishing and optimizing long-term molluscan cell culture systems and advancing next-generation in vitro models for fundamental and translational research. Position Description · Develop, optimize, and
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to optimize cellular performance. The Fellow will develop an independent research project aligned with the broader goals of the collaboration and will have opportunities to lead publications, make scientific
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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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; 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