An Intelligent Defense Against Hackers

Data normally flows safely through networks, but cyberattacks can subtly disrupt it—often unnoticed. Traditional NIDS based on synthetic data may miss such threats. This project combines machine learning with logic and differentiable logics trained on real data to create monitoring systems with digital intuition that detects even well-hidden attacks.

MARTIN – Deep Learning and Automation of Imaging-Based Quality of Seeds and Grains

Today, manual visual inspection of grain is still one of the most important quality assurance procedures throughout the value chain of bringing cereals from the field to the table. 

Together with industrial partners, this project aims to develop and validate a method of automated imaging-based solutions that can replace subjective manual inspection and improve performance, robustness and consistency of the inspection.