Host: KUL (BE) Main supervisor: Prof. Mathias Verbeke (KUL, BE) Co-supervisors/mentors: Prof. Philippe Besnier (IETR/CNRS, FR), ing. Ronny Deseine (Barco, BE), Prof. Tim Claeys (KUL, BE) Duration: 36 months Required profile: Electrical Engineering or Computer Science Desirable skills/interests: Machine Learning, Artificial Intelligence, Electromagnetism Objectives: DC13 aims to leverage AI for real-time anomaly detection in the electromagnetic environment, where anomalies are rare deviations from normal behavior. Due to the challenge of gathering a comprehensive dataset of anomalies, a semisupervised approach will be devised, using normal data to construct a model representing regular electromagnetic behavior. The project will combine “EMI footprints”, which capture normal variations through characteristic curves and statistical distributions, with traditional machine learning techniques (e.g., one-class support vector machines) and deep learning techniques (e.g., autoencoders/ transformers) to detect anomalies. By integrating these AI techniques with EMI footprints, DC13 seeks to develop an efficient methodology for real-time anomaly detection in the electromagnetic environment. Secondments (2-4 months in total): Thales (NL) and IETR (CNRS, FR)