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into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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. Candidates will perform research on agentic AI, foundational modeling, optimization, and control of multiagent autonomous systems with an application in renewable energy and power grids, in addition to working
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fellow with a Ph.D. in electrical engineering, applied mathematics, or related field. Candidates will perform research on agentic AI, foundational modeling, optimization, and control of multiagent
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models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees. Research areas include Representation Learning, Machine learning and Optimization
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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computer science, statistics, operations research, or related computational fields. As part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel
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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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(D^3) Institute and the LISH/Data Science & AI Operations Lab seek enthusiastic Postdoctoral Fellows skilled in computer science, statistics, operations research, or related computational fields. As
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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