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.
Smartification of water quality and safety monitoring for water distribution systems
| Code | i3WaterSDC3 |
|---|---|
| Host Institution | THE UNIVERSITY OF SHEFFIELD School of Mechanical, Aerospace and Civil Engineering |
| Location | SHEFFIELD, S10 2TN, United Kingdom |
| Supervisor(s) | Main Supervisor: Dr Isabel Douterelo Soler(USFD, UK) Co-supervisor: Dr Manuel Herrera (UNEW,UK) & Dr David Ayala-Cabrera(UCD, Ireland) Industrial Mentor – Experts: Dr Paul Gaskin (DCWW, UK) |
| Research Field | Artificial Intelligence |
| Contract type | Fixed term contrac |
| Application Deadline | september 30th, 2026 |
Description
Research Objectives
The main research goal of iWaterS is to provide, for the first time, rational analyses and explainable intelligent decision support of WDS to increase resilience to day-to-day incidents and to extreme events in the context of climate change such as floods or droughts, through new interdisciplinary and integral approaches for exploiting datasets (on-line, off-line), intelligent models (data-driven, numerical) and simulation results (digital twins, multiagent systems).
The specific research objective of this offer is To develop intelligent monitoring and predictive models that integrate microbial, environmental and hydraulic information for the early detection of water quality risks and improved resilience of drinking water distribution systems.
Therefore, the candidate will contribute with the the following subobjectives:
1) Obtain a robust data set (microbial, environmental and hydraulic parameters) combining data from experimental tests and field work in real networks and service reservoirs, essential to modelling of resilience and vulnerability to microbial contamination.
2) To develop cutting edge methodologies such as graph convolutional neural networks to detect and predict contamination events in response to infrastructure failures and extreme weather events.
3) To provide insights into the dynamics of biofilms, and interdependencies between microorganisms and infrastructure over time, under different scenarios.
Expected Results:
1) Development of geometric deep learning, time-series data mining, and agent-based approaches incorporating microbial information for WDSs.
2) Criticality performance indicators for WDSs.
3) Tools for infrastructure vulnerability analysis and predictive maintenance models.
Requirements
Education level
Master Degree
Skills / Qualifications
- Experience (or interest) in urban water sector, including research, industry or public sector.
- Aptitude for research in drinking water systems, including machine-learning and ecological modelling.
- Experince with Python, R or related programming languages for analysis of large datasets.
- Experience (desirable) with molecular work including DNA sequencing and bioinformatics
- Willingness to collaborate with other researchers, industry and end-users.
- The candidate is expected to publish her/his research in scientific journals and conferences.
- Strong organisational skills.
Required languages
English – C1
