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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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. 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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, engineering, physics, or similar fields by the expected start date. Additional Qualifications Applicant should ideally have some experience in electric power systems, artificial intelligence, and optimization
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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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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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PhD in theoretical neuroscience, physics, computer science, or related fields is required. Applicants must demonstrate strong analytical and numerical skills. Additional Qualifications Special
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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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with strong analytical and numerical skills, and backgrounds in physics, theoretical neuroscience, applied mathematics, computer science, engineering, or related fields. Experience in relevant research
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multidisciplinary team members to optimize experimental protocols and analyze data. Publish research findings in peer-reviewed journals and present results at conferences and seminars. Mentor graduate and
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seeking a highly motivated postdoctoral researcher to play a key role in optimizing the performance of the Rubin Observatory system. Our near-term goal is to optimize the performance of the Rubin