i3WaterS › Doctoral positions

Uncertainty-Aware Optimisation for Robust Water Distribution System Operation

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 uncertainty-aware optimisation methodologies that improve operational decision-making in drinking water distribution systems by explicitly accounting for uncertainty in hydraulic models and operational data.

The candidate will contribute with the following subobjectives:

Objectives: 1) Develop a methodology to identify the sources of uncertainty based on Value of Information concepts, to improve decision-making in WDS operation/planning. 2) Advance computational methods for uncertainty-aware optimization to assist operational real-time decision-making.

Expected Results:

1) Methodology and codes to identify sources of uncertainty that are worth resolving.
2) Methodology and codes to robustly optimize WDS operation, considering uncertain inputs.
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, together with strong written and spoken English. Applicants must not already hold a doctoral degree and must satisfy the MSCA mobility rule.

Experience with hydraulic modelling, optimisation libraries, uncertainty quantification, data science, machine learning or Value of Information methods is desirable. Candidates should demonstrate analytical ability, independent research capacity, scientific writing skills, initiative, collaboration and readiness to work in an international and multidisciplinary environment.

Required languages

English – C1