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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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of advanced technologies. This includes developing cutting-edge AI/ML models and datasets; learning how to take advantage of unparalleled computing resources in the academic environment by optimizing AI/ML
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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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to, the following: Data Analysis and Programming Develop study questions and analytic approach (including literature searches and data searches using various sources) Optimize, test, and debug model code Clearly
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relationship managers by leading partnership protocols and ensuring alignment with organizational goals. Collaborate with the Senior Director to continuously optimize team workflows and partner engagement
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questions and analytic approach (including literature searches and data searches using various sources) Optimize, test, and debug model code Clearly document model development, structure, logic, coding
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?s Comms. writing team to effectively synthesize content across platforms Publish, maintain, and update the Alumni section of the HKS website, including making recommendations and driving optimization
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of unparalleled computing resources in the academic environment by optimizing AI/ML models including scaling models across a large set of GPUs; building or optimizing LLMs to tackle new, complex tasks; developing
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formats and spaces. Manually set up A/V systems for staff meetings and special events. Work with faculty to determine optimal technology integration in classroom settings and provide walkthroughs
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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