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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic
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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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; 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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no more than two additional pages of tables, references, and graphs, describing the proposed research for the fellowship year. The names and email addresses of 2 referees, who will be asked via a
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machine learning and deep learning methods, including architectures such as Transformers, RNNs, and CNNs, and related models used for sequence, image, graph, or multimodal data. Demonstrated experience
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machine learning and deep learning methods, including architectures such as Transformers, RNNs, and CNNs, and related models used for sequence, image, graph, or multimodal data. Demonstrated experience