DEEP-GRAPH: Design and Theory of Deep Graph Learning
Deadline: 02/02/2026
Scientific-Disciplinary Group
01/INFO-01 - Informatics
Description
The goal of the project is the advancement of the research of deep neural networks, in the fieldof adaptive processing of graph data (Deep Graph Learning). The project includes the following strongly interconnected fundamental research topics: a) introduction of highly efficient DGL models to reduce the energy impact and increase the sustainability of DGL models; b) increase the expressiveness of DGL models, obtaining better predictive performances on existing tasks and enabling the application of DGL innovel tasks where current methods do not achieve satisfying performances; c) extending the scope of DGL application, not only in terms of the considered tasks but also on the considered setting: e.g., dynamic (spatio/temporal) graphs pose several challenges and are not well studied to date, as well as the generation of graphs characterized by hierarchical structures; d) increase our theoretical understanding of existing DGL methods, as well as the ones developed in the project.
Compensation
22,500 Euro
Job posting website
Number of positions
1
Funding body
Università degli Studi di Padova
How to apply
View the original posting on the MUR website: Go to MUR website