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Harvard University is offering a Postdoctoral Fellow position in Geometric Machine Learning at the Harvard John A. Paulson School of Engineering and Applied Sciences. This role focuses on conducting innovative research that connects Geometry and Machine Learning, working on geometric structures in data.
The ideal candidate will have a Ph.D. in Mathematics, Computer Science, or a related field. Applications will be reviewed on a rolling basis.
Title: Postdoctoral Fellow in Geometric Machine Learning
School: Harvard John A. Paulson School of Engineering and Applied Sciences
Department/Area: Applied Math
A postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research at the intersection of Geometry and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees. 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 the possibility of extension.
Applications will be reviewed on a rolling basis.
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Pay offered to the selected candidate is dependent on factors such as rank, years of experience, training or qualification, field of scholarship, and accomplishments in the field.
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