[Neuroimaging] Open PhD Position on Machine Learning for Brain Connectivity in Clinical Neuroscience

[Paolo Avesani]

OPEN PHD POSITION ON MACHINE LEARNING FOR BRAIN CONNECTIVITY IN
CLINICAL NEUROSCIENCE

We are pleased to announce the opening of one PhD position in
neuroinformatics at the International Doctoral School in Information
Engineering and Computer Science (IECS) of the University of Trento,
Italy. The PhD Grant is funded by the Neuroinformatics Laboratory
(NILab) of Fondazione Bruno Kessler (FBK).  This project is in
collaboration with the School of Medicine of the University of Trento
and the Division of Neurosurgery, S. Chiara Hospital, Trento.

Clinical neuroscience is playing a key role in the understanding of
the brain with data of pathological alterations. The detection of
anomalies in the brain structure and function is a crucial step not
only for diagnosis and prognosis but also to decode the connectome of
the human brain. Data driven approaches are providing promising
results to characterize the patterns of the healthy brain. The
challenge is to disentangle the intrinsic interindividual differences
in the brain structure and function with respect to alterations
related to cognitive impairment.

The research objective is to investigate the most innovative
techniques of Artificial Intelligence, such as geometric deep
learning, to translate the knowledge of connectivity structures from a
healthy population to the individual patients of a clinical study. The
ultimate goal is the development of computational methods to support
the detection of altered structures in the connectome affected by
brain disorders.

The successful candidate will have a MSc in computer science,
biomedical engineering, physics, or any STEM discipline. Hands-on
training in neuroimaging data analysis and/or software tools for brain
connectivity will be highly valued. For reference to recent work of
relevance to this project, please see (Astolfi et al., 2023),
(Legarreta et al., 2021).

The position is for a 3 year PhD program (Nov. 1, 2023- Oct. 31,
2026). 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-excellence-in-three-areas).

Why choose Trento?
(http://www.unitn.it/en/ateneo/1629/why-choose-the-university-of-trento)

Important dates:
Deadline for application: 5 September 2023
Beginning of PhD program: 1 November 2023

Apply now:
https://iecs.unitn.it/education/admission/call-for-application
(grant A7 - Deep Learning for Clinical Neuroscience)

You are kindly invited to contact in advance Paolo Avesani
(paolo.avesani at unitn.it).

Recent related publications:

- Pietro Astolfi, Ruben Verhagen, Laurent Petit, Emanuele Olivetti,
Jonathan Masci, Davide Boscaini, Paolo Avesani, (2023) Supervised
Tractogram Filtering Using Geometric Deep Learning",  Medical Image
Analysis,Volume 89.
 https://doi.org/10.1016/j.media.2023.102893

- Gabriele Amorosino, Denis Peruzzo, Daniela Redaelli, Emanuele
Olivetti, Filippo Arrigoni,  Paolo Avesani, (2022) DBB - A Distorted
Brain Benchmark for Automatic Tissue Segmentation in Paediatric
Patients, Neuroimage, 260.
https://doi.org/10.1016/j.neuroimage.2022.119486

- 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, (2021) Classifyber, a robust streamline-based linear
classifier for white matter bundle segmentation", Neuroimage.
https://doi.org/10.1016/j.neuroimage.2020.117402

- Paolo Avesani, Brent McPherson, Soichi Hayashi, Cesar F. Caiafa,
Robert Henschel, Eleftherios Garyfallidis, Lindsey Kitchell, Daniel
Bullock, Andrew Patterson, Emanuele Olivetti, Olaf Sporns, Andrew J.
Saykin, Lei Wang, Ivo Dinov, David Hancock, Bradley Caron, Yiming Qian
& Franco Pestilli, (2019) The open diffusion data derivatives, brain
data upcycling via integrated publishing of derivatives and
reproducible open cloud services, Nature Scientific Data, 6(69).
https://doi.org/10.1038/s41597-019-0073-y

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