Postdoctoral Fellow in Riemannian Optimization: The Harvard John A. Paulson School of Engineering and Applied Sciences is seeking a skilled postdoctoral fellow to work within the Geometric Machine Learning Group led by Prof. Melanie Weber. This position focuses on researching Riemannian Optimization, ideal for candidates with a strong academic background and a keen interest in this specialized field.
Designation:
Postdoctoral Fellow
Research Area:
Riemannian Optimization
Location:
Harvard University, Cambridge, MA
Eligibility/Qualification:
- A Ph.D. in Mathematics, Applied Mathematics, Computer Science, or a related field must be completed by the start of the appointment.
Job Description:
This one-year postdoctoral position offers the opportunity to engage in cutting-edge research on Riemannian Optimization. The successful candidate will contribute to the Geometric Machine Learning Group’s projects, leveraging their expertise in the field. The start date for this position is flexible, and there is a possibility for an extension based on performance and available funding.
How to Apply:
Interested candidates should submit the following application materials:
- Curriculum Vitae (CV)
- Two-page Research Statement outlining current and future research interests
- Three Reference Letters
- Copies of two publications representative of their work and research interests, ideally related to Riemannian Optimization.
Applications can be submitted through the provided Harvard job application portal.
Last Date to Apply:
Applications will be reviewed on a rolling basis, and the position will remain open until filled.
For additional details on research and publications, please visit the Geometric Machine Learning Group’s website or contact Prof. Melanie Weber at mweber@seas.harvard.edu.
Harvard University is an equal opportunity employer and welcomes applications from all qualified individuals.
