Stephen Jose Hanson jose at rubic.rutgers.edu
Mon Oct 24 13:51:31 CEST 2016

NIH Post-doctoral Fellowship in BRAIN Initiative: Theories, Models and
Methods for Analysis of Complex Data from the Brain

Applications for an NIH-funded post-doctoral position are invited for
the Laboratory of Dr. Stephen José Hanson’s in the Department of
Psychology and in RUBIC (Rutgers Brain Imaging Center) at
Rutgers University, Newark, N.J. USA. The focus of research and
training is in the area of  computational neuroscience, and the
development of brain network models based on graphical model selection
using dynamical search procedures and the modeling of brain signals
arising from neuroimaging data (e.g. fMRI).

These projects will be performed in the lab of Dr. Hanson and in
conjunction with Dr. Glymour’s (CMU) causal discovery research group.
The ideal candidate will have a strong background in
computation, statistics, probability theory, multivariate analysis and
signal processing. Programming skill sets ( e.g., C++, R, Matlab,
Debian, python) would be key to a successful candidate. Additionally,
having background in neuroscience, neuroimaging or brain science more
generally would be important to a successful application.
This is a 2 year position with a 3 rd year possible. Starting date is
flexible and with earliest start by December 1. Applications will be
accepted until a candidate is hired.
Inquiries should be addressed to Stephen José Hanson (jose at rubic.rutger
s.edu). Please also send applications including a CV, statement of
research interests, and the names and full contact details of three
referees to jose at rubic.rutgers.edu.

Relevant publications include:

Ramsey J., Hanson S.J. Hanson C., Halchenko Y.O., Poldrack R.A.,  &
Glymour C. (2010). Six problems for causal inference from fMRI.
Neuroimage, 49, 1545-1558.

Hanson, C., Hanson, S.J., Ramsey, J. & Glymour, C. (2013)   Atypical
effective connectivity of social brain networks in individuals with
autism. J. Brain Connect. 3(6):578-89.  

Hanson, S. J. & Halchenko, Y. O. (2008). Brain reading using full brain
support vector machines for object recognition: there is no face-
identification area. Neural Computation, 20, 486-503

Poldrack, R.A., Halchenko, Y., & Hanson, S.J. (2010). Decoding the
large-scale structure of brain function by classifying mental states
across individuals. Psychological Science, 20, 1364-1372 

Ramsey, J., Glymour, M., Sanchez-Romero, R. and Glymour, C. (in Press)
A million variables and more: The fa st greedy search algorithm for
learning high dimensional graphical  causal models, with an application
to functional magnetic resonance images.  International Journal of Data
Science and Analytics.

Rutgers is an equal employment opportunity employer. All qualified
applicants will receive consideration for employment and will not be
discriminated against on the basis of race, color, religion, sex,
sexual orientation, gender identity, national origin, veteran status,
or disability.

More information about the Comp-neuro mailing list