[Neuroimaging] Post-doc position: Learning functional-connectivity biomarkers of pathologies

Gael Varoquaux gael.varoquaux at normalesup.org
Tue Dec 8 18:55:06 EST 2015

Dear Nipy community,

I have an exciting post-doc position to fill that I beleive can be a much
interest to people of this community.


Post-doc position: Learning functional-connectivity biomarkers of pathologies

Parietal (https://team.inria.fr/parietal/) is looking to fill a
post-doc position on learning biomarkers from functional

Scientific context

The challenge is to use resting-state fMRI at the level of a population
to understand how intrinsic functional connectivity captures pathologies
and other cognitive phenotypes. Rest fMRI is a promising tool for
large-scale population analysis of brain function as it is easy to
acquire and accumulate. Scans for thousands of subjects have already been
shared, and more is to come. However, the signature of cognitions in this
modality are weak. Extracting biomarkers is a challenging data processing
and machine learning problem. This challenge is the expertise of my
research group. Medical applications cover a wider range of brain
pathologies, for which diagnosis is challenging, such as autism or
Alzheimer's disease.

This project is a collaboration with the Child Mind Institute
(http://www.childmind.org/), experts on psychiatric disorders and
resting-state fMRI, and coordinators of the major data sharing
initiatives for rest fRMI data (eg ABIDE).

Objectives of the project

The project hinges on processing of very large rest fMRI databases.
Important novelties of the project are:

- Building predictive models to discriminate multiple pathologies in
  large inhomogeneous datasets.

- Using and improving advanced connectomics and brain-parcellation
  techniques in fMRI.

Expected results include the discovery of neurophenotypes for several
brain pathologies, as well as intrinsic brain structures —eg functional
parcellations or connectomes— that carry signatures of cognition.

Desired profile

We are looking for a post-doctoral fellow to hire in spring. The ideal
candidate would have some, but not all, of the following expertise and

* Experience in advanced processing of fMRI

* General knowledge of brain structure and function

* Good communication skills to write high-impact neuroscience publications

* Good computing skills, in particular with Python. Cluster computing
  experience is desired.

A great research environment

The work environment is dynamic and exiting, using state-of-the-art
machine learning to answer challenging functional neuroimaging question.
The post-doc will be employed by INRIA (http://www.inria.fr), the lead
computing research institute in France. We are a team of computer
scientists specialized in image processing and statistical data analysis,
integrated in one of the top French brain research centers, NeuroSpin
(http://i2bm.cea.fr/dsv/i2bm/Pages/NeuroSpin.aspx), south of Paris. We
work mostly in Python. The team includes core contributors to the
scikit-learn project (http://scikit-learn.org), for machine learning in
Python, and the nilearn project (http://nilearn.github.io), for
statistical learning in NeuroImaging.

In addition, the post-doc will interact closely with researchers from the
Child Mind Institute (http://www.childmind.org), with deep expertise in
brain pathologies and in the details of the fMRI acquisitions. Finally,
he or she will have access to advanced storage and grid computing
facilities at INRIA.

**Contact**: gael.varoquaux at inria.fr, bertrand.thirion at inria.fr

**Application**: Interested candidate should send CV and motivation letter

    Gael Varoquaux
    Researcher, INRIA Parietal
    NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
    Phone:  ++ 33-1-69-08-79-68
    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux

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