Research Project in Biological and Machine Intelligence at Carnegie Mellon University, USA

Publish Date: Dec 29, 2015

CMU is looking for postdocs and graduate students interested in biological and machine intelligence. It should be a very exciting collaboration between computer science/statistical machine learning and neuroscience.

A Research Project in Biological and Machine Intelligence at Carnegie Mellon University:

Researchers at Carnegie Mellon University, in collaboration with researchers at multiple universities, including Johns Hopkins University, the University of Pittsburgh, the University of North Carolina are launching an inter-disciplinary collaborative research program in Biological and Machine Intelligence to study the neural mechanisms of visual intelligence and learning in the brain. The general objective is to develop new biologically inspired or constrained machine learning and artificial intelligence systems. The current collaborative project will involve the following five areas of research: (1) neurally constrained machine learning and vision theory, (2) computational vision and mathematical neural circuit modeling, (3) large-scale calcium imaging in mice, (4) large scale multi-electrode electrophysiology in primates, and (5) advanced neural big data analysis. The current project is focused on the study of perceptual learning and inference mechanisms in the mammalian visual cortex in the theoretical framework of compositional theory and probabilistic graphical models. We are recruiting postdocs and graduate students with interests and/or expertise in each of the above five areas of research. Participating faculty involved in the current project include:

Tai Sing Lee (Project director, CMU, Computer Science and and Center for the Neural Basis of Cognition (CNBC), )

Sandra Kuhlman (CMU, Biological Science and CNBC,

Alan Yuille (Johns Hopkins, Center for Cognition, Vision and Learning,

Steve Chase (CMU, Biomedical Engineering and CNBC), )

Brent Doiron (University of Pittsburgh, Mathematics and CNBC, )

Abhinav Gupta (CMU, Robotics, )

Robert Kass (CMU, Statistics and CNBC,

Gary Miller (CMU, Computer Science, )

Ruslan Salakhutdinov (CMU, Machine Learning, )

Spencer Smith (University of North Carolina, Cell biology and Physiology, )

Byron Yu (CMU, Electrical Engineering and CNBC, ).

Applicants should have strong interest or background in neuroscience. Desired background and expertise includes one or more of the following: neurophysiology, preferably with background in mice or primate experiments, computer science/engineering/physics with an interest or experience in modeling neural circuits, development of bio-inspired computer vision and machine learning systems, or application of statistical and machine learning techniques to large-scale neural data analysis. Interested postdoc applicants should send their CV, and research and career interest statements to AND the specific participating faculty member(s) whom they are interested in working with. We will review applications on a rolling basis. Most hiring decision will be decided by the end of February 2016.

Similar Opportunities



Study Levels


Opportunity Types


Eligible Countries


Host Countries

United States