Postdoctoral Fellow or Research Associate
Baylor College of Medicine, Houston, TX, USA
Posted: 2 May 2008

 

Position for Postdoctoral Fellow or Research Associate to study the neural circuits that govern visual processing and learning using multi-tetrode recordings and two-photon imaging.

Position at the level of postdoctoral fellow or research associate is available to study the neuronal mechanisms that govern perception and learning. Our focus is to understand the mechanisms of these processes at the circuit level in the visual system. To this end, we employ methods that allow us to study the properties of network of neurons in vivo during behavior. Specifically, we use chronic arrays of tetrodes and in vivo two-photon imaging combined with psychophysical and computational methods.

We have developed the capability to record from the same individual neurons across multiple days in awake, behaving primates that gives us the unique opportunity to study how circuits reorganize in vivo during learning. Our lab has also strong interest to develop new technologies to record from interconnected neural circuits using novel imaging and multi-electrode recording methods and also to develop methods – including molecular tools - to manipulate the activity of circuits in vivo. The research scientists for these positions will have the opportunity to study brain mechanisms of behavior using one or a combination of these methods in either rodent or primate animal models.

Consideration of applications will begin immediately, and will end when the position is filled. Salary is competitive and will be commensurate with experience and qualifications. Baylor College of Medicine is an Affirmative Action/Equal Opportunity employer and is committed to cultural diversity and compliance with the Americans with Disabilities Act.


Contact Info:



Andreas Tolias, Ph.D
Assistant Professor
Department of Neuroscience
Baylor College of Medicine
Suite S553
Houston, Texas 77030
atolias@cns.bcm.edu
http://neuro.neusc.bcm.tmc.edu/?sct=emp_tolias