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the project directors and collaborators to develop data-driven and economically grounded frameworks for understanding how AI-enabled control, optimization, and market design can support large-scale
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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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at finale.seas.harvard.edu and our group’s webpage https://dtak.github.io/ We work on probabilistic models, reinforcement learning, and interpretability + human factors. Basic Qualifications Candidates are required to have
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solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast
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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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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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team. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/erapid-multiplexed-electrochemical-sensors-for-fast-accurate-portable-diagnostics/. What you’ll
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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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data Clear scientific writing and communication; a track record of publications Experience with causal inference Bonus: experience with explainable ML, optimization/decision strategies, or work with EHR