Postdoctoral Fellowship in Computer Vision at Harvard University

Registration website

School Harvard John A. Paulson School of Engineering and Applied Sciences

Department/Area Computer Science

Position Description

The Visual Computing Group (https://vcg.seas.harvard.edu/) in the School of Engineering and Applied Sciences (SEAS) at Harvard University seeks motivated postdoctoral fellows with a Ph.D. in computer science/engineering or a related field to work on applied research projects in computer vision and deep learning. We are working on a wide range of exciting research areas, including image enhancement, video understanding, visual question answering, and biomedical image analysis. In addition to working closely with graduate students and collaborators on research projects, the candidate is expected to actively participate in writing grant proposals.

Basic Qualifications

Ph.D. in computer science/engineering or a related field to work on applied research projects in computer vision and deep learning.

Additional Qualifications

Candidates should have a substantial publication history in top computer vision/machine learning venues and good teamwork and communication skills.

Special Instructions

A complete application must include a cover letter, curriculum vitae, and three letters of reference.

Contact Information

Evie Santana-Nola
Contact Email [email protected]

Equal Opportunity Employer

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required 3
Maximum Number of References Allowed 5

Required fields are indicated with an asterisk (*).

Required Documents

1. Curriculum Vitae
2. Cover Letter

Optional Documents

1. Statement of Research
2. Publication
3. Publication 2
4. Publication 3
5. URL
6. Publication 4
7. Publication 5