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. For more details on our research and recent publications, see the Geometric Machine Learning Group's website.

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

Additional Qualifications

Special Instructions - 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.

Salary Range - $67,600 – $91,826 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.

Minimum Number of References Required - 3

Maximum Number of References Allowed - 3

Keywords

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

Applicant Documents - Required Documents: Curriculum Vitae, Cover Letter, Statement of Research, Publication, Publication 2. Optional Documents: Publication 3.