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Details Title Postdoctoral Fellow in Energy System Optimization and Digitization School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position Description
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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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for understanding how AI-enabled control, optimization, and market design can support large-scale decarbonization, grid modernization, and the integration of distributed and flexible energy resources. Research topics
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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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statistical inference/optimization, and will have the chance to mentor both undergraduate and graduate students in these areas (as it relates to joint projects). Special Instructions Required application
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-accurate-portable-diagnostics/. What you’ll do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries
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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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do: Design, fabricate, characterize, and optimize electrochemical biosensing technologies for real-time detection. Develop and implement novel surface chemistries to improve sensor performance
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of optimizing pipelines for large-scale genomic projects. Special Instructions Required documents: CV Research summary of PhD work. Cover letter describing your interest in the lab and initial ideas for new
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, and optimization to address challenges created by the operationalization of AI within partner organizations. The Postdoctoral Fellows will play a pivotal role within the LISH/Data and Science Operation