[visionlist] Research in Biological and Machine Intelligence at Carnegie Mellon University

A Research Project in Biological and Machine Intelligence at Carnegie Mellon

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), tai@cs.cmu.edu )

Sandra Kuhlman (CMU, Biological Science
and CNBC, skuhlman@andrew.cmu.edu)

Alan Yuille (Johns Hopkins, Center for
Cognition, Vision and Learning, alan.l.yuille@gmail.com)

Steve Chase (CMU, Biomedical Engineering
and CNBC),  schase@cmu.edu )

Brent Doiron  (University of Pittsburgh, Mathematics and
CNBC,  bdoiron@pitt.edu )  

Abhinav Gupta (CMU, Robotics, abhinavg@cs.cmu.edu )

Robert Kass (CMU, Statistics and CNBC, kass@andrew.cmu.edu)

Gary Miller (CMU, Computer Science, glmiller@cs.cmu.edu )

Ruslan Salakhutdinov (CMU, Machine
Learning, rsalakhu@cs.toronto.edu )

Spencer Smith (University of North
Carolina, Cell biology and Physiology, slab@email.unc.edu

Byron Yu  (CMU,
Electrical Engineering and CNBC, byronyu@cmu.edu

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 aibrain@cmu.edu 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. Most 


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