Job offer

i3WaterS brings together leading universities, research centres, technology developers and water utilities across Europe. The project will train 15 Doctoral Candidates to develop innovative AI-driven solutions for intelligent, resilient and sustainable water systems, contributing to the digital transformation of one of the most critical infrastructures for society. The risk of water scarcity due to climate change and human activities is real. i3WaterS stands for Intelligent, Innovative, Integrative Water Systems and addresses the urgent need to optimize water resources management by providing a comprehensive solution to upgrade, optimally operate and maintain water distribution systems (WDSs). For the first time, a unique holistic approach will find the key interrelationships between external, day-to-day and extreme, factors and WDS failures, to advise actions and protocols to make WDSs robust and reliable. At research level, i3WaterS project focuses on the integration of data, specific expert knowledge and computational simulations tools, introducing the most advanced data-driven and artificial intelligent techniques beyond the State of the Art, that plugged in a newly developed intelligent decision support system (IDSSs), working as an umbrella for a set of 14 independent solutions that enables assisting the WDSs management into scientifically driven decision-making. The incorporation of artificial intelligence (AI) will help to increase the autonomy of some parts of the WDS, those suitable under a paradigm of maximum security, safety and robustness, and the project will take care to frame this autonomy in a global and general concept of intelligent assistance, including human validation in each steps where it makes sense 15 Doctorate Candidates will learn from a network of experts on network monitoring, data management, algorithms, AI, modeling, microbiology, ethics and industrial partners, and by participating in a specifically designed training programme, they will develop the required cross competencies to find, test and innovate over a solution that will be fundamental to meet the sustainable development goals on water. Just as important, i3WaterS provide a new generation of internationally connected professionals with unique skills for the development of thriving careers in the critical infrastructures.

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).

he objective of the project is the protection of drinking water distribution systems (WDSs) and their consumers from the adverse impacts of climate change and human activities. For the first time, a unique holistic approach will identify the key interrelationships between external, day-to-day, and extreme factors and WDS failures, advising actions and protocols to make WDSs robust and reliable. This PhD research focuses on developing climate-resilient water networks by integrating virtual sensors, digital twins, multi-criteria decision analysis (MCDA), and explainable AI (XAI) into one unified framework.

The specific research objective of this offer is To develop climate-ready Artificial Intelligence methodologies that enhance the resilience, adaptability and long-term sustainability of drinking water distribution systems under both normal and extreme operating conditions.

The candidate will contribute with the following subobjectives: Objectives: 1) Build tools for climate-resilient WDS objectives, 2) ensure both real-time responsiveness and long-term sustainability, 3) Adapt a WDS to extreme events and mitigate degraded service levels, 4) Decrease the system vulnerability and increase the resilience by design. Expected Results: 1) An explainable digital twin, which is of reduced order to be fast, and connected to data for accuracy, 2) Optimal sensor placement to complete the existing ones and virtual sensors, 3) proactive monitoring and predictive maintenance.

Training Programme The training proposed by i3WaterS will uniquely integrate decades of knowledge, expertise & achievements in disciplines such as civil and computer engineering, hydroinformatics, geomechanics, applied mathematics, multiobjective optimization, high performance computing, big data, artificial intelligence, modelling, and data management, including soft skills facilitated by academic and non-academic partners. Apart from the PhD thesis done under a multidisciplinar and international supervisory panel composed by an advisor, a coadvisor from a second i3WaterS university and an industrial mentor linked to a real water facility, the program includes an International Doctoral School with six training chapters that take place under an international mobility structure (Barcelona (Spain), Dublin (Ireland), Bordeaux (France), Delft (The Netherlands), Brussels (Belgium), NewCastle (UK)). The contents of the training programme include the most relevant and advanced topics related with smart resilient WDSs and soft skills for the researchers and professionals of the future. The following topics are included in these training chapters:

• Artificial Intelligence and Machine Learning.
• Explainable AI and Trustworthy AI.
• Knowledge Representation and Semantic Technologies.
• Multi-Agent Systems and Intelligent Decision Support Systems.
• Digital Twins and Smart Water Systems.
• Innovation, entrepreneurship and technology transfer.
• Scientific communication and transferable skills. International mobility will easy connections and visits to water facilities all over Europe, and industrial secondments will give a realistic perspective.

Two International Secondments in other second real water facilities will allow extensive testing and validation of thesis findings to guarantee real contribution to the state of art. These Secondments into industrial partners are included with two aims: testing the PhD findings in a different water facility from the one supporting the project development, and to allow providing specialised training to the water utilities staff., 46320 + two secondments:Secondment 1. 4-months secondment at IHE (supervised by Dr. Alfonso) starting in M17. Secondment 2. 2-months secondment at CET (mentor Dr Arnaldos) for purposes of testing and validating findings. M34. Secondment 1. 4-months secondment at IHE (supervised by Dr. Alfonso) starting in M17. The DC will receive a specialized training in efficient critical infrastructure (Digital Twins) and optimal operation with IHE, TUD, VIT and will conduct/receive a data collection/mentoring at VIT and BW. Interaction with DC4, DC9. Secondment 2. 2-months secondment at CET (mentor Dr Arnaldos) for purposes of testing and validating findings. M34. Eligibility criteria

All candidates who cannot provide proof of the required academic qualification will be immediately excluded from the selection process.

The selection process will have 2 steps: 1st step: Curriculum Vitae All CV lines should be accompanied by documents to prove and substantiate their content. Those that are not will not be considered.

Curriculum vitae will be evaluated according to the following general criteria: The maximum score for candidates who meet all the requirements set out in the job offer will be 10 points. Each category will be scored between 0 and 10. · Required specialization. · Required academic training. · Technical competencies. · Organizational competencies. · Professional experience. · Any aspect of the candidate's professional profile that the selection committee considers particularly relevant.

CV Final Score = 0.1required specialization + 0,2 Required Academic Trainer+0,2Technical Competences + 0,1Organizational Competences + 0,3* Professional Experience + 0,1*Candidates professional Profile

The minimum qualification to pass the CV step is 5.

2nd Step: only candidates who have obtained a score of 5 or higher in their CV will be shortlisted for the interview. The interview will be evaluated according to the following criteria:

Each category will be scored between 0 and 5

· Suitability to the functional competencies of the position. · Relevance of professional experience. · Any aspect of the candidate's professional profile that the selection committee considers particularly relevant.

Interview Maximum Score= 0.4suitability to the functional competencies + 0.4relevance of professional experience+0.2*aspect of the candidate's professional profile

The minimum qualification to pass the interview step is 3 Eligible candidates will be ranked from highest to lowest score, which will be the selection criterion.

Selection process

Once the application submission period has ended, the secretary of the selection committee may contact applicants to request any mandatory documentation that has not been provided, or to ask for additional documentation needed to evaluate the application.

The Evaluation Committee will carry out an initial assessment of the eligible candidates' CVs and, if deemed appropriate, will invite those who pass this stage to take part in tests and/or interviews. The date and location of the interviews and/or tests will be set by the committee and will be communicated in advance to the selected candidates via the email address provided in their application. . Candidates must be available to carry out the test and/or interview using an online platform. Additional comments

Please note that the position is based in a laboratory located in a restricted area, which requires a pre-employment background check that can take up to eight weeks to complete.

Master Degree or equivalent

Research Field Computer science

Education Level Master Degree or equivalent

Research Field Mathematics » Applied mathematics

Education Level Master Degree or equivalent, Modelling and optimization skills Basic knowledge of hydraulics in water networks Background in engineering and/or mathematics Python, R, Java or related programming languages Excellent analytical and problem-solving skills Ability to work independently and in a team Scientific writing skills and motivation to promote results