The VUB Artificial Intelligence Lab combines fundamental and applied research in artificial intelligence, with a strong focus on multi-agent systems, reinforcement learning, hybrid and symbolic AI, and knowledge representation and reasoning. Building on this broad AI expertise, the Lab develops intelligent, autonomous, explainable, and trustworthy AI solutions for complex real-world systems, including applications in water management and environmental infrastructure.
Topic 3. Smart, Secure and Adaptive Water Infrastructures for Sustainable Water Management
We aim to join the Water4All consortium to apply our AI expertise to real-world water challenges and co-develop innovative solutions. We are particularly interested in projects where AI can support resilient, efficient, and adaptive water systems, with opportunities to test and validate solutions in operational settings. The VUB AI Lab brings expertise in both fundamental and applied artificial intelligence research to water management challenges, bridging advanced AI methodologies with real-world infrastructure, environmental, and hydrogeological data. Our research interests include: 1. Multi-Agent Systems (MAS) & Autonomous Control: Decentralized reinforcement learning and multi-agent systems for the autonomous operation of smart water networks, adaptive urban flood mitigation, and the optimization of pumping and groundwater recharge operations. 2. Cyber-Physical Security & Anomaly Detection: Machine learning approaches for real-time anomaly detection and the identification of abnormal behavior in water infrastructure and cyber-physical systems. 3. Explainable AI (XAI) & Decision Support: Explainable predictive modelling, uncertainty-aware scenario analysis, and human-in-the-loop decision-support systems to support transparent, robust, and trustworthy operational and strategic water-management decisions.
smart water grids and water management, water resources modeling, operational decision support, core ai & technical methodologies, dynamic demand and load forecasting, anomaly detection, multi-agent autonomous control