TWO OPENINGS FOR POST-DOC POSITIONS Two post-doc calls have been opened (see details below) in the context the following project funded by the Department of Information Engineering, University of Padova: Title of the project Personalized whole brain models for neuroscience: inference and validation Abstract of the project: Contemporary neuroscience has embraced network science to study the complex and self- organized structure of the human brain, with the promise of addressing key societal issues such as neural degeneration and treatment of neurological and psychiatric diseases and damages. These objectives will be pursued in this highly interdisciplinary project following a data-driven model based approach: our final goal is to design novel algorithmic solutions for data-driven inference of whole-brain mesoscale dynamical models as well as to develop and validate (or invalidate) models based on both data driven as well as methodological studies. Ideally, these models should provide solid grounds to develop (i) novel individual-level features for predicting cognitive and behavioral deficits originated by brain lesions or neuro- degeneration and (ii) simulation tools for designing personalized treatments such as stimulation. Team: The post-docs will work in an interdisciplinary team composed of Engineers, Psychologists, Neurologists and Physicists Salary: Approx. 25Keuro/year gross Duration: 20 months (can possibly be extended to 32 months) Opening date: approx. March 2nd, 2020 Closing date: approx. April 2nd, 2020 Link PostDoc#1: https://www.dei.unipd.it/node/26525 Link PostDoc#2: https://www.dei.unipd.it/node/26510 Contacts: Prof. Alessandro Chiuso Department of Information Engineering University of Padova chiuso@dei.unipd.it Prof. Alessandra Bertoldo Department of Information Engineering University of Padova bertoldo@dei.unipd.it POST DOCTORAL POSITION # 1 TITLE: Development and analysis of novel models and methods for effective connectivity in whole-brain network models Activity: The Post-Doc will develop and refine algorithms for estimation of effective connectivity models from neuroimaging data (fMRI) providing also an in-depth comparison between different classes of methodologies (e.g. DCM-like models and “Granger’’ causality type models). He/she will also actively collaborate in the validation studies and work in close interaction with the external collaborators (neurologists, neuroscientists, physicists). Profile: The ideal candidate should have a recent PhD in engineering, applied mathematics/physics, statistics, computer science, and related fields. He/she must have demonstrated experience in complex system modeling and advanced analytic techniques (e.g. multivariate approaches, machine learning, graph theory etc.). Strong analytical/mathematical skills are a requirement. Experience in one or more areas of neuroimaging will be plus. Programming skills (C, C++, Python, Matlab) are not a prerequisite, but a clear advantage. Moreover, the candidate must be highly motivated and creative individual with the ability to work in a dynamic, multi-disciplinary research environment and be willing to interact with both experimental and theoretical neuroscientists. POST DOCTORAL POSITION # 2 TITLE: Analysis and validation of novel models and methods for effective connectivity in whole-brain network models Activity: The activities will be mainly related to the preprocessing and analysis of neuroimaging data as well as validation, via systematic statistical testing, of effective connectivity models on animal data as well as on stroke patients. In particular, the post-doc will be involved in the design of experiments and conduct human neuroimaging research on normal brain organization and changes in network architecture among patients with stroke, with an emphasis on effective and functional connectivity network mapping. The post-doc will compare the human results with those he/she will obtain working with whole- brain rs-fMRI signals from the animal model. Profile: The position is open to recent PhDs in applied mathematics/physics, computer science, engineering, statistics and related fields, with demonstrated ability to conduct high impact research. The successful applicant will have expertise in anatomical MRI, dMRI and/or rs-fMRI analysis, familiarity with control theory and system identification, time-series analysis, statistics and graph theoretic and network modeling. Expertise in vivo electrophysiology signals analysis is also desirable. Experience with neuroimaging analysis programs (ANTs, FSL, SPM, FreeSurfer or other relevant programs), and strong knowledge on programming (e.g. good command of scripting, Python and Matlab) is also expected. Clearly, the successful candidate will be part of a diverse and multidisciplinary group including engineers of different specialties, neuroscientists, physicists, therefore a strong attitude and flexibility in team-working are required to foster cross-breeding and fertilization among the different disciplines involved in the project.