About the Project
The main research goal of i3WaterS is to provide, for the first time, rational analyses and explainable intelligent decision support of Drinking Water Distribution Systems (DWDS) 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, intelligent models 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 DWDS.
Therefore, the candidate will contribute with the the following sub-objectives:
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 DWDSs.
2) Criticality performance indicators for DWDSs.
3) Tools for infrastructure vulnerability analysis and predictive maintenance models.
Entry requirements
- A minimum 2:1 honours degree (or international equivalent) in Civil and Environmental Engineering (water), Environmental Sciences or a related subject.
- Aptitude for research in drinking water systems, including machine-learning and ecological modelling.
- Familiarity with Python, R or related programming languages for analysis of large datasets
- Experience with molecular work including DNA sequencing and bioinformatics is highly desirable.
- Willingness to collaborate with other researchers, industry and end-users.
To be admitted, candidate must meet the requirements for access (including English language) to a doctoral programme in the School of MAC at The University of Sheffield.
Latest possible start date: 01/01/2027
Length of funded period: 3 years
Funding Notes
This project is funded by Horizon Europe Marie Skłodowska-Curie Doctoral Network i3WaterS. It covers tuition fees plus additional stipend(s).
