Data-Driven Anomaly Detection Using Machine Learning for Trustworthy Cyber-Physical and Industrial Systems

I seek research partners focused on advanced anomaly detection using machine learning and artificial intelligence. The project will develop novel, explainable, and trustworthy AI methods capable of identifying anomalies in complex and dynamic environments, including cyber-physical systems, industrial processes, IoT infrastructures, healthcare, and smart cities.

The network will combine expertise in machine learning, deep learning, explainable AI, data analytics, and edge/cloud computing to design robust detection algorithms, benchmark them on real-world datasets, and validate them in practical use cases. Particular emphasis will be placed on uncertainty estimation, privacy-preserving AI, federated learning, and energy-efficient models.