Title of the Project
“Analisi multimodale della connettività cerebrale e modelli predittivi del comportamento basati su machine learning” (in English: “Multimodal analysis of brain connectivity and predictive modelling of behavior based on machine learning”), Research Supervisor Prof. Marco Zorzi
Purpose of the Research Grant
The purpose of this Research Grant, which is funded by the European Union – NextGenerationEU – through MUR PNRR fundings in the framework of the project MNESYS “A multiscale integrated approach to the study of the nervous system in health and disease” – PE00000006, Missione 4 “Istruzione e ricerca”, Componente 2 “Dalla ricerca all’impresa”, Investimento 1.3 “Creazione di Partenariati Estesi alle università, centri di ricerca, alle aziende per il finanziamento di progetti di ricerca di base”, M4C2 Spoke 2, CUP: B83C22004960002, is to grant research into:
The aim of the research fellowship is to develop analytical methods that integrate multimodal neuroimaging data at the connectome level and then derive predictions on behavior and cognition.
Activity: The research fellow will work with and contribute to the acquisition of new multimodal data (MRI, fMRI, EEG) pre-processed from healthy subjects and neurological patients. The research fellow will (i) develop an automated procedure to pre-process and analyze data from different sources in a common reference space; (ii) compare brain connectivity derived by different modalities or methods (e.g. functional vs. effective connectivity) and assess its predictivity on behavior and cognition using also machine learning methods; (iii) evaluate methods and possible benefits of integrating multimodal data.
Profile: The ideal candidate should have: (a) experience in analyzing functional magnetic resonance imaging data; (b) experience in using machine learning on neuroimaging data; (c) advanced data analysis and programming skills in Python/Matlab.
Deadline for submitting applications
1 October 2024 at 1:00 pm CEST