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. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a strong background in this area, as well as a genuine interest in continuing such work
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mathematics, economics, operations research or a related field. Additional Qualifications Candidates who have a strong mathematical background in reinforcement learning and/or control (e.g., optimal control
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, operations research or related field. Additional Qualifications Candidates who have a strong mathematical background in reinforcement learning and/or control (e.g., optimal control, decentralized control, and
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mathematical background in reinforcement learning and/or control (e.g., optimal control, decentralized control, and/or adaptive control) with a strong desire to make an impact on energy/power grids are preferred
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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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magnetic response. Development of machine learning methods for exchange-correlation functionals. Current work in the group is focused on improvements and performance optimizations for the recently developed
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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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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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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