Post-doctoral fellow position(s) to the study of the natural visual environments of infants and young children and their implications for visual, cognitive and language development and machine learning at Indiana University. The larger collaborative project involves analyses of the properties of a very large corpus of head camera images (500 million) collected by infants 1 to 24 months of age with respect to low, mid and higher level properties, the examination of the statistical structure of early learned visual categories (and their in-home naming by parents), the design and implementation of computational experiments using machine learning and computer vision models, as well as experiments with infants testing novel predictions from these analyses and models. The post-doctoral fellow(s) will take part in the intellectually rich cognitive science, computational neuroscience, vision, developmental, and computer science communities at Indiana University under the Emerging Areas of Research Initiative titled Learning: Brains, Machines and Children. Collaborators on the larger project include Linda Smith, David Crandall, Franco Pestilli, Rowan Candy, Jason Gold, and Chen Yu.
This is an excellent opportunity for individuals with past training in one or more of the following: infant statistical learning, infant visual development (including face and object perception), visual neuroscience, adult vision, computer vision. Other areas of training with computational and/or experimental backgrounds will be considered.
Please apply to Linda Smith, , with Visual Environments in the subject heading by sending a cover letter stating your interest in this project, your cv, and a research statement. References will be requested after initial contact.
The filling of these position(s) are open in their timing; although we hope to appoint one position this fall, January or this spring are also possible start dates.