Dear DiPy community, We are pleased to announce the opening of one PhD position in neuroinformatics at the International Doctoral School in Cognitive and Brain Sciences (CIMeC) of the University of Trento, Italy. The PhD Grant is funded by the Neuroinformatics Laboratory (NILab) of Fondazione Bruno Kessler (FBK). The position is part of the “Neusurplan” project, an integrated approach to neurosurgery planning based on multimodal and longitudinal data. Neuroimaging methods, like structural, functional, and diffusion magnetic resonance imaging (MRI), can be used to investigate the anatomical and functional connectivity of the brain. In this project, the candidate will pursue research on machine learning methods for neuroimaging data analysis to study and characterize brain connectivity, with applications to longitudinal studies and clinical practice. The ideal candidate should have a mixed background in neuroimaging techniques and numerate disciplines, like computer science, engineering, physics, or mathematics. This project is in collaboration with the Division of Neurosurgery, S. Chiara Hospital, Trento (IT). The position is for a 4 year PhD program (Nov. 1, 2020- Oct. 31, 2024). Courses are in English. The salary is starting at approximately €1.200/mo., net. An additional personal budget of around €5.000 is provided for research and mobility. The University of Trento ranks among top Italian Universities ( https://www.unitn.it/en/ateneo/1636/rankings). Fondazione Bruno Kessler ranks first among the Italian research centers in Engineering and Computer Science ( https://magazine.fbk.eu/en/news/fbk-ranks-1st-in-italy-for-scientific-excell... ). Why choose Trento? ( http://www.unitn.it/en/ateneo/1629/why-choose-the-university-of-trento) Important dates: Deadline for application: 25 May 2021 Evaluation interview: 28 June 2021 Beginning of PhD program: 1 November 2021 Apply now: https://www.unitn.it/en/ateneo/1942/announcement-of-selection https://www.unitn.it/drcimec/105/2021-topic-specific-grants-and-descriptions You are kindly invited to contact in advance Emanuele Olivetti ( olivetti@fbk.eu) and Paolo Avesani (paolo.avesani@unitn.it). Recent related publications: - Giulia Bertò, Daniel Bullock, Pietro Astolfi, Soichi Hayashi, Luca Zigiotto, Luciano Annicchiarico, Francesco Corsini, Alessandro De Benedictis, Silvio Sarubbo, Franco Pestilli, Paolo Avesani, Emanuele Olivetti, "Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation", Neuroimage, 2021, https://doi.org/10.1016/j.neuroimage.2020.117402 - Gabriele Amorosino, Denis Peruzzo, Pietro Astolfi, Daniela Redaelli, Paolo Avesani, Filippo Arrigoni, Emanuele Olivetti, "Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy", Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology (MLCN 2020), https://doi.org/10.1007/978-3-030-66843-3_2 - Pietro Astolfi, Ruben Verhagen, Laurent Petit, Emanuele Olivetti, Jonathan Masci, Davide Boscaini, Paolo Avesani, "Tractogram filtering of anatomically non-plausible fibers with geometric deep learning", Medical Image Computing and Computer Assisted Intervention (MICCAI 2020), https://doi.org/10.1007/978-3-030-59728-3_29 -- Emanuele Olivetti, Ph.D. Senior Research Scientist at Bruno Kessler Foundation (FBK-ICT) NeuroInformatics Laboratory (NILab) http://nilab.fbk.eu Center for Mind and Brain Sciences (CIMeC), University of Trento Via delle Regole 101 - 38123 Mattarello (Trento), ITALY olivetti@fbk.eu - +39 0461314179 emanuele.olivetti@unitn.it - +39 0461282760 -- -- Le informazioni contenute nella presente comunicazione sono di natura  privata e come tali sono da considerarsi riservate ed indirizzate  esclusivamente ai destinatari indicati e per le finalità strettamente  legate al relativo contenuto. Se avete ricevuto questo messaggio per  errore, vi preghiamo di eliminarlo e di inviare una comunicazione  all’indirizzo e-mail del mittente. -- The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. If you received this in error, please contact the sender and delete the material.