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Fully funded PhD position on machine learning for neuroimaging data analysis at Istituto Italiano di Tecnologia (IIT)

Country : Italy

Website : http://ict.unitn.it

Description

We are seeking candidates for a PhD position entitled “Computational Methods for Brain Connectivity Analysis”.
The research will aim at developing novel approaches based on machine learning for the analysis of structural and functional brain connectivity from neuroimaging data (e.g., fMRI, rsMRI, DTI, MEG, EEG, etc.). The aim is to better understand the brain and characterize the neurological and neurodegenerative diseases.
The successful candidate will have the opportunity to work in an interdisciplinary environment.
The applicant should have a background in machine learning and pattern recognition methodologies with a keen interest in neuroscience.
The PhD grant is jointly supported by Fondazione Bruno Kessler (FBK) and Istituto Italiano di Tecnologia (IIT). The research activity will take place at the Neuroinformatics Laboratory (NILab, https://nilab.fbk.eu/) and the Pattern Analysis and Computer Vision Laboratory (PAVIS, http://pavis.iit.it/). NILab is an interdisciplinary laboratory raised as joint initiative between the Centre for Information Technology of FBK and the Centre for Mind/Brain Sciences of University of Trento (CIMeC, http://www.cimec.unitn.it).
Interested candidates must apply for the PhD grant following the instructions in the call published by Università degli studi di Trento (see the application webpage http://ict.unitn.it/application/ict_doctoral_schoo...) referring to position A5.
Deadline for the formal application is August 31, 2017, hrs. 04.00 PM (Italian time, GMT +2)
Interested candidates are also invited to contact Paolo Avesani (avesani-AT-fbk.eu) and Diego Sona (diego.sona-AT-iit.it) by emailing their CV and a short statement of interest in this position.
For further information on the position and the application process refers to the following links
http://ict.unitn.it
http://ict.unitn.it/application/project_specific_g...

Last modified: 2017-08-24 22:32:24