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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status. Create a Job Match for Similar Jobs About Harvard
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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an environment that is diverse, inclusive and respectful. Learn more about our lab here: https://bioniclab.seas.harvard.edu/ We are recruiting fellows from diverse backgrounds interested in solving tough problems
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Institute or working on machine learning, artificial intelligence, or computational neurobiology at Harvard. A research proposal of no more than 3 pages (1500 words, exclusive of references) outlining plans
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managing very large datasets; Machine learning skills; Writing papers for management and economics journals; Interest in reskilling initiatives; Working with partner organizations or companies. Basic
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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or more computational environments for statistical analysis (e.g., MATLAB, Stata, R, or Python); Creating and managing very large datasets; Machine learning skills; Writing papers for management and
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degree in Statistics/Economics/Applied Math/Computer Science or related fields Knowledge of regression analysis, statistical inference, familiarity with machine learning/prediction tools Experience with
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Details Title Postdoctoral Fellow in Deep Learning Theory and/or Theoretical Neuroscience School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position