A multi-criteria approach to support water circularity at regional level.

Urban water networks are systems used to transport wastewater and potable water to and from centralized treatment plants. This model has improved sanitation and reduced pollution but falls short of addressing current challenges like rapid urbanization and climate change, including increased flooding and water scarcity. UNEP projects a potential 40% gap between demand and supply by 2030. Shifting from the model of abstracting, using, and disposing to more efficient, circular water use is essential. Implementing water circularity systems is hindered by the lack of a comprehensive framework and tools tailored to local contexts and technologies. Existing indicators based on the 5Rs (reduce, reuse, recycle, reclaim, restore) mainly focus on water volume, often neglecting water quality, resilience, and treatment needs, limiting practical application. Broader benefits, as ecosystem services inherent to the deployed technology, should be considered in a holistic approach. While optimization methods have been applied at various scales, they often overlook system dynamics, demand variability, and stakeholder preferences. In addition, variation in water demands and quality needs and preferences for different stakeholders is rarely addressed in the literature. Excluding these topics associated with the implementation of new systems may generate environmental or social conflicts.

The aim of this project is to develop and validate a model to help decision makers select and implement water circularity based on the local context, including preferences of stakeholders, available technologies, and their wider ecosystem services. It would go beyond the state of the art of evaluating how much water circularity each scenario could achieve by determining which technologies to implement to guarantee safe and resilient water usage while maximizing the delivery of wider co-benefits and minimizing costs. The proposed multi-objective approach, based on optimization and machine learning algorithms, is used for defining optimal configurations, including water treatment, distribution, and management facilities. It considers urban, agricultural, and industrial constraints associated with water circularity as well as dynamics in demands, quality, and preferences of social, government, and industrial actors in multi-criteria decision-making environments. This approach allows identifying conflicts and synergies around economic performance, water and energy savings, and delivering ecosystem services.

IMT Atlantique is one of the top 10 engineering schools in France, and one of the top 500 universities in the world in THE World University Ranking. It is a general engineering grande école financed by the Ministry of Economy, Finance and Industrial and Digital Sovereignty, and the first Institut Mines Télécom "Mines-Telecom" Technological university, founded on January 1st, 2017, from the merger of Mines Nantes and Télécom Bretagne.

Candidates with backgrounds and master’s degrees in fields such as chemical engineering, biochemical engineering, environmental engineering, and process systems engineering are welcome to apply. The ideal candidate for this position should possess the following skills and qualities:

• Knowledge of hydrological systems and water treatment.
• Experience in process and system optimization and modelling.
• Interest in topics such as integrated water management and treatment, multi-objective optimization strategies, game theory, and machine learning.
• Proficiency in English.

Desirable:

• Experience in water treatment systems.
• Experience in data science and machine learning.