Postdoctoral Fellow in Geometric Machine Learning

Postdoctoral Fellow in Geometric Machine Learning

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. Applications will be reviewed on a rolling basis.

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

To apply, please submit the following materials:

• CV
• Research Statement outlining your current and future research interests
• Three Reference Letters
• Copies of two publications representative of your work and research interest

SEAS is dedicated to building a diverse and welcoming community. 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.

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes. Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy.