Lab Location

Suite 320C, 3401 Walnut St.

Office Location

Suite 312C, 3401 Walnut St.

Psychology Department

University of Pennsylvania

(215) 573-6528

Mailing Address


3401 Walnut - Suite 400A

Philadelphia, PA 19104-6228

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Natural Vision Laboratory

Principal Investigator: Johannes Burge

Graduate students:

Post-doctoral researchers:

Undergraduate students:

  1. Contact

Joining the Laboratory:

  1. CV

We are always interested in hearing from candidates with an interest in vision and perception research. Backgrounds in visual psychophysics, computational modeling, statistics, probability theory, and display technology are desired. A willingness to think and work hard are required.

Prospective post-doctoral researchers should email Professor Burge a curriculum vitae, a research statement, electronic copies of two papers (published or in preparation), and the names, emails, and phone numbers of three people qualified to recommend you and your work.

Prospective graduate students should email Prof. Burge a curriculum vitae, and a research statement. Prospective students must apply through an appropriate PhD program (Psychology, Neuroscience Graduate Group, Engineering).

Prospective undergraduate student researchers should email Prof. Burge a curriculum vitae, a research statement, a transcript, previous research experience (if any), a list skills potentially useful to the lab, and a description of goals after receiving your degree. Applicants should expect to spend 1-3 years in the lab and to publish the results of their work.

Post-doctoral Position Available Now!

The successful applicant will have an interest in visual processing of natural images, depth perception, and underlying neurophysiology.

The successful applicant will also have a background in psychophysics, computational modeling, optics, and/or image processing.

Other desirable qualities:

Strong math skills (Probability theory, Fourier analysis, 3D geometry)

Strong programming skills (Matlab, C/C++, PsychToolbox, OpenGL)

Desire to tinker with high-end display equipment

Familiarity with machine learning, numerical optimization