Recruitment

  • PhD positions   Deep Graph Representation Learning on non-uniform 3D objects 

    This thesis focuses on deep learning, with an emphasis on learning graph representations. Graphs are widely used in many applications, providing a versatile representation for non-regular objects, including 3D meshes, as an alternative to traditional methods such as CNNs or image segmentation models like U-net. This thesis explores graph neural networks (GNNs) for modeling non-regular 3D objects, such as 3D meshes. Unlike CNNs, GNNs are designed to handle graph-type data, making them more suitable for representing 3D meshes.

  • PhD positions   From AI to Propositional Logic: Conversion to SAT and Model Analysis 

    This thesis topic aims to create a bridge between artificial intelligence (AI) solutions and formal techniques of classical propositional logic. It seeks to develop generic methods for transforming AI models into propositional formulas, focusing on the satisfiability problem, known as the SAT. This approach looks to leverage the significant advancements made in the field of SAT solvers. A key aspect of this work is identifying the important features of AI models that can be efficiently translated into SAT representations.