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.
Hybrid AI for Sustainable Management of Water Distribution Systems
| Code | i3WaterSDC7 |
|---|---|
| Host Institution | UNIVERSITE DE BORDEAUX Institut de mécanique et d’ingénierie (I2M) |
| Location | BORDEAUX, Postcode 33000 France |
| Supervisor(s) | Main Supervisor: Dr Olivier Piller (UBx,France) Co-supervisor: Prof Astrid Decoene (UBx,France) Industrial Mentor – Experts: Dr Olivier Chesneau (REBM,France) |
| Research Field | Artificial Intelligence |
| Contract type | Fixed term contract — Full time, 37.5 h/week |
| Application Deadline | August 16th, 2026 |
Description
Research Objectives
The objective of the project is the protection of drinking water distribution systems (WDSs) and their consumers from the adverse impacts of climate change and human activities. For the first time, a unique holistic approach will identify the key interrelationships between external, day-to-day, and extreme factors and WDS failures, advising actions and protocols to make WDSs robust and reliable.
The purpose of this PhD research is to develop tools and methods for sustainable management of water distribution networks. Specifically, operational management will aim to regulate pressure and velocity within the distribution system. This will help to achieve two primary objectives: ensuring water quality and minimizing leakage.
The specific research objective of this offer is To develop advanced hydraulic and numerical modelling techniques that improve the simulation, calibration and operational management of drinking water distribution systems under complex and dynamic conditions.
The candidate will contribute with the following subobjectives:
Objectives: 1) Increase the detection threshold and location of leakage flows for real case studies, using only pressure data;. 2) Sustainable operation of a WDS network (with pressure and flow regulation) to reduce background leakage, sedimentation, and pipe fatigue. 3) Adapt a WDN to extreme events and mitigate degraded service levels.
Exp. Results: 1) New hybrid AI algorithms for a better detection and localization of leakage. 2) Gain measurements for additional sensors and actuators. 3) New hydraulic model handling flow velocity and pressure constraints.
Requirements
Education level
Master Degree
Skills / Qualifications
Modelling and optimization skills
Knowledge of machine learning
background in mathematics
Experience with Python or other programming languages
Excellent analytical and problem-solving skills
Ability to work independently and in a team
Scientific writing skills and motivation to promote result
Machine Learning and Data Mining
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