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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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, 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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, network telemetry, network security, network compression methods, and optimizing network performance for machine learning applications. The ideal candidate will be interested in both building real systems
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, network telemetry, network security, network compression methods, and optimizing network performance for machine learning applications. The ideal candidate will be interested in both building real systems
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that can be easily swapped or adjusted to optimize sensing for different environments and use cases. Preferred start date as soon as possible but flexible. Documents should include a full CV, cover letter
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secondary research intended to refine the definition of people-centered care, measure the status of people-centered across different settings, identify optimal measurement approaches, and examine the
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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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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