intelligent, innovative, integrative Water Systems
AI-driven solutions for intelligent, resilient and sustainable water systems
i3WaterS brings together universities, research centers, technology developers
and water utilities across Europe to support the digital transformation of
critical water infrastructures.
Long-Term Planning Under Uncertainty for Resilient Water Infrastructure
| Code | i3WaterSDC8 |
|---|---|
| Host Institution | STICHTING IHE DELFT INSTITUTE FOR WATER EDUCATION Hydroinformatics and Socio-Technical Innovation |
| Location | DELFT, 2611 AX, Netherlands |
| Supervisor(s) | Main Supervisor: Dr Leonardo Alfonso (IHE, The Netherlands) Co-supervisor: Prof Zoran Kapelan (TUD, The Netherlands) Industrial Mentor – Experts: Dr Mario Castro-Gama (VIT, The Netherlands) |
| Research Field | Artificial Intelligence |
| Contract type | Fixed term contract — Full time, 37.5 h/week |
| Application Deadline | September 20th, 2026 |
Description
Research Objectives
The PhD candidate will develop uncertainty-aware, model-based multi-objective optimisation methods to support robust operational decision-making in drinking-water distribution systems. The research will examine how uncertainty in hydraulic models, operational data and demand forecasts affects decisions, identify which uncertainty sources are most valuable to reduce, and validate the resulting methods using a real water-utility case.
The specific research objective of this offer is To develop intelligent Digital Twin methodologies that integrate hydraulic modelling, optimisation and real-time operational data for predictive and adaptive management of drinking water distribution systems.
The candidate will contribute with the following subobjectives:
Objectives: 1) To build a methodology to integrate resilience, robustness, flexibility, sustainability and system intelligence into a single decision-making process for long term planning for utilities. 2) To couple a water model of the critical infrastructure with optimization and decision-making tools, considering uncertainty into the metrics of robustness and flexibility.
Expected Results: 1) Methodology for planning criteria integration. 2) Framework incorporating models, optimization and decision-making within the above methodology. 3) Application of the methodology in a real case and evaluation.
Requirements
Education level
Master Degree
Skills / Qualifications
A relevant Master’s degree in Hydroinformatics, civil or environmental engineering, water engineering, applied mathematics, operations research, computer science or a related field. Applicants should have knowledge of hydraulics, water-distribution-system modelling, optimisation and programming in Python, as well as strong written and spoken English. Applicants must not already hold a doctoral degree and must satisfy the MSCA mobility rule.
Experience with long-term infrastructure planning, multi-objective, robust or stochastic optimisation, uncertainty analysis, digital twins, data science or machine learning is desirable. Candidates should demonstrate systems thinking, analytical and programming ability, independent research capacity, scientific communication skills and a collaborative and proactive attitude.
Required languages
English – C1
i3WaterS is a doctoral network project funded by the European Union under the HORIZON.1.2 – Marie Skłodowska-Curie Actions (MSCA) ( HORIZON_MSCA-101227354-i3WaterS).
For more information cordis.europa.eu.
i3WaterS contact: i3waters.management@upc.edu