This PhD research project at Wetsus, the European centre of excellence for sustainable water technology, focuses on the development of AI-assisted monitoring methods for drinking water networks. Drinking water networks are vital infrastructures, but aging pipelines face increasing risks from pressure surges, ground movement, construction activities, vegetation growth, and changing weather conditions. These factors contribute to leaks and pipe bursts, resulting in water losses, service disruptions, and higher operational costs. In the Netherlands, water scarcity is becoming a concern, making proactive asset management and loss reduction increasingly urgent.

The project is part of the Wetsus Smart Water Grids program and aims to shift from reactive leak detection to proactive monitoring by creating integrated, physics-informed, data-driven methods that provide early insight into pipe failure risk. Research challenges include combining heterogeneous data sources such as operational hydraulic data, environmental information, asset records, and observations from modern sensing technologies. The project will address the relationships between hydraulic operations, environmental conditions, and observed pipe failures, as well as interpreting indirect observations from remote sensing platforms like drones and satellites. Linking these observations to physical processes affecting pipes in the subsurface is another key challenge.

Innovation opportunities arise from integrating physics-based knowledge with AI-assisted data analysis and data fusion techniques. By combining multiple data sources and sensor observations with hydraulic models and digital twins, the project aims to develop predictive tools to identify increasing failure risks and support proactive monitoring and maintenance strategies for drinking water networks.

As a PhD candidate, you will develop innovative methods to detect and predict failure risks in drinking water distribution and transport networks. You will combine operational hydraulic data, asset information, and environmental data to explore how these factors relate to pipe failures. The project also involves investigating the potential of sensing technologies on moveable platforms, such as drones or satellites, to detect anomalies indicating defects or soil disturbances around pipelines. Your work will include data analysis, AI model development, and validation through case studies, experiments, or pilot studies in collaboration with water utilities and industrial partners.

Applicants should have an MSc degree in civil engineering, water technology, environmental engineering, data science, or a related field, and demonstrate excellent English proficiency. The ideal candidate is interested in interdisciplinary research, enjoys working with real-world data, and is motivated to find solutions for practical problems. The project is supervised by Professor Peter van Heijster and Assistant Professor Xiaodong Cheng (Wageningen University & Research), and Dr. D.R. Yntema (Wetsus).

Applications must be submitted in English via the application webpage before the deadline. Only complete applications will be considered. For detailed guidelines, refer to the applicant guide. The position is based in Leeuwarden, Netherlands, and offers a collaborative research environment with opportunities to work alongside academic and industrial partners.