Job offer

The Universitat Politècnica de Catalunya (UPC), Barcelona, Spain, is recruiting a Doctoral Candidate (DC) within the Horizon Europe Marie Skłodowska-Curie Doctoral Network i3WaterS - Intelligent, Innovative and Integrative Water Systems. The successful candidate will be enrolled in a PhD programme at UPC and will work under the supervision of Dr. Javier Vázquez-Salceda, internationally recognised expert in Artificial Intelligence, Knowledge Engineering and Explainable Decision Support Systems.

i3WaterS brings together leading universities, research centers, 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).

The specific research objective is developing innovative Artificial Intelligence methodologies to improve short-term water demand forecasting in Drinking Water Distribution Systems (DWDSs). The project aims to exploit individual consumption data and consumer behaviour modelling to support more efficient, adaptive and sustainable water network operation through an Intelligent Decision Support System (IDSS). The research objectives are to:

• Collect, curate, analyze and exploit anonymized individual water consumption data from real drinking water distribution systems, ensuring data quality, privacy and interoperability. ç
• Characterize the consumption through Intelligent clustering techniques and automatic conceptual interpretation for consumer profile creation.
• Design semantic models and consumer typologies that capture different demand patterns and support the interpretation of consumption dynamics.
• Investigate and advance agent-based simulation models capable of reproducing consumer behaviour and generating profile-driven short-term water demand forecasts under different operational scenarios.
• Integrate data-driven and knowledge-based AI techniques into an Intelligent Decision Support System (IDSS) to support demand forecasting and assess the impact of infrastructure modifications and operational interventions on water consumption.
• Validate the proposed methodologies using real-world datasets from European water utilities and evaluate their robustness, scalability and transferability across different operational contexts.

Expected Results: 1) Dataset/s from WDSs gathered, filtered and analyzed. 2) Ontology of types of consumers and normal consumption patterns per type of consumer 3) Research progress in the use of profile-driven agent-based simulation models for short-term water demand prediction. 4) Deployment of a IDSS for short-term water demand prediction 5) Deployment of an intelligent recommender for personalized alerts to consumers.

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., 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.

Research Field Computer science » Other

Education Level Master Degree or equivalent

Research Field Engineering » Computer engineering

Education Level Master Degree or equivalent, * Machine Learning and Data Mining

• Knowledge-Based Systems
• Data-driven models
• Profilng & behaviour modelling
• Explainable Artificial Intelligence (XAI)
• Multi-Agent Systems
• Decision Support Systems
• Python, R, Java or related programming languages
• Data integration and interoperability
• Documenting in Latex
• Teamwork, Candidates must also comply with all applicable eligibility requirements and regulations of the Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks programme.

Languages ENGLISH

The planned remuneration for DN 2024 is as follows:

Salary: €33,667.67 gross per year Living allowance: €6,235.47 per year Family allowance: €5,796.35 gross per year

• two secondments:
• Secondment 1. 3-months secondment at UC (supervised by Dr Mouthon). The DC will receive a specialized training in water demand forecasting modeling and optimal operation in collaboration with UC and will conduct/receive a data collection/mentoring at AC.
• Secondment 2. 2-months secondment at VIT (mentor Dr Castro-Gama) for testing and validating the findings

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 with documentation to prove and evidence the content of the line.Those non proved will not be considered