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Details Title Postdoctoral Fellow in Geometric Machine Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Applied Math Position Description A
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opportunities, benefits, community events, and other resources available via the office of Graduate and Postdoctoral Success at BU: https://www.bu.edu/postdocs/ Skills and qualifications A PhD in epidemiology
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or deadline for this position has passed and new applications are no longer accepted. *** Position Description Applications are invited for a postdoctoral fellowship at ENS Lyon in the field of machine learning
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, United States of America [map ] Subject Area: Physics / Machine Learning Appl Deadline: 2025/12/02 04:59 AM (posted 2025/09/10 05:00 AM, listed until 2026/03/11 03:59 AM) Position Description: Apply Position Description
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be involved in the three-year project “High Dimensional Hierarchical Optimization methods for Machine Learning and Stochastic Optimal Control”. Background or expertise in one or more of the following
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(https://www.hsph.harvard.edu/lin-lab/ ), Professor of Biostatistics and Professor of Statistics. The postdoctoral fellow will develop and apply statistical, machine learning (ML), and AI methods
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Fall 2026 opening for a Postdoctoral Research Associate to work with the recently established Simons Collaboration on the Physics of Learning (physicsoflearning.org). The Simons Collaboration is
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 3 days ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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Postdoctoral Fellowship Program, visit our program page: https://climate.duke.edu/what-were-doing/scales-postdoctoral-fellows-program/ KEY RESPONSIBILITIES: RESEARCH COLLABORATION: The SCALES Postdoctoral Fellow
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evidenced by a strong publication record in one or more of the following areas: random matrix theory, stochastic processes, neural networks, machine learning, and sparse data structures • Proficiency in