PhD Candidate in Hydroinformatics: Uncertainty-Aware Optimisation for Robust Water Distribution System Operation IHE Delft Institute for Water Education in Netherlands

IHE Delft Institute for Water Education is recruiting a PhD Candidate in Hydroinformatics for research on uncertainty-aware optimisation for robust water distribution system operation within the Horizon Europe Marie Skłodowska-Curie Doctoral Network i3WaterS (Intelligent, Innovative and Integrative Water Systems).

The project focuses on developing a model-based, multi-objective optimisation methodology for drinking-water distribution systems that explicitly accounts for uncertainty in hydraulic models and operational data. The research will combine hydraulic and water-quality models, monitoring data, expert knowledge, digital twins, optimisation, and artificial intelligence to improve operational decision-making under uncertainty. A key aim is to identify which uncertainty sources matter most, including through Value of Information concepts, and to advance computational methods for robust and real-time optimisation.

The candidate will be hosted by the Hydroinformatics and Socio-Technical Innovation Department at IHE Delft in Delft, Netherlands. The supervisory team includes Associate Professor Leonardo Alfonso (main supervisor, IHE Delft), Dr Oscar Coronado Hernandez (co-supervisor, University of Cartagena), and Dr Claudia Quintiliani (industrial mentor, Brabant Water).

The position is a 48-month PhD appointment with a full-time workload of 38 hours per week. The contract starts on 1 January 2027, with an initial 18-month period and a go/no-go decision within the first year. The remuneration follows IHE Delft employment conditions and the applicable MSCA Doctoral Network allowances. The mobility plan includes secondments at the University of Cartagena and Cetaqua.

Applicants should have a Master’s degree in Hydroinformatics, civil or environmental engineering, water engineering, applied mathematics, operations research, computer science, or a closely related field. Strong knowledge of hydraulics and hydraulic modelling of drinking-water distribution systems is required, along with experience in Python and preferably optimisation libraries and water-network modelling software. Experience with data science, machine learning, uncertainty quantification, or Value of Information methods is an advantage. English proficiency and the ability to write academic texts in English are required.

Applications must be submitted in English by 20 September 2026 at 23:59 CEST. Applicants should upload a CV, motivation letter, abstract of MSc thesis, and two contactable referees via the Apply button on the vacancy page. Email applications will not be considered.