Postdoctoral Fellow in Geometric Machine Learning at Harvard University in Cambridge, Massachusetts

Job Description:

Harvard University


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 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. The preferred start date is July 1, 2025, though there is some flexibility.

For more details on our research and recent publications, see the Geometric Machine Learning Group's website: https://weber.seas.harvard.edu
For questions, please email mweber@seas.harvard.edu .

Applications will be reviewed on a rolling basis, starting December 15. The position will remain open until filled.

Basic Qualifications: A Ph.D. in Mathematics, Computer Science, or a related field, by the start of the appointment.


Special Instructions: To apply, please submit the following materials:

1. CV
2. Research Statement outlining your current and future research interests
3. Three Reference Letters
4. Copies of two publications representative of your work and research interest

SEAS is dedicated to building a diverse and welcoming community.

Contact Information: For more details on our research and recent publications, see the Geometric Machine Learning Group's website: https://weber.seas.harvard.edu

Contact Email: mweber@seas.harvard.edu

Equal Opportunity Employer: Harvard is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status.

Minimum Number of References Required: 3

Maximum Number of References Allowed: 3


Supplemental Questions: Required fields are indicated with an asterisk (*).

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