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. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a strong background in this area, as well as a genuine interest in continuing such work
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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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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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economics and seeking hands-on research experience before graduate school. Research Areas • Tariffs and trade policy • Stablecoins and digital currencies • Optimal policy design in open economies • Broader
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machine learning methods for computational materials physics and chemistry. Projects include: The aim is to develop generalized equivariant neural network models NequIP and Allegro for machine learned
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/Area Computer Science Position Description Accepting applications for postdoctoral position in Reinforcement Learning, Probabilistic Methods, and/or Interpretability. Information on the lab can be found
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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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Details Title HMS - Postdoctoral Fellow in Biomedical Informatics (Park Lab) School Harvard Medical School Department/Area Biomedical Informatics Position Description The candidate will have the
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cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical data across multi-institutional research networks, with a focus on the analytical and