Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields

The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco's frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa.

Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally.

CEDoc - UM6P – CAES -IWRI :

INTEGRATED APPROACH TO GROUNDWATER ASSESSMENT IN MOROCCO: COMPUTATIONAL HYBRID MODEL

About  UM6P:

Mohammed VI Polytechnic University is an institution dedicated to research and innovation in Africa and aims to position itself among world-renowned universities in its fields. The University is engaged in economic and human development and puts research and innovation at the forefront of African development. A mechanism that enables it to consolidate Morocco's frontline position in these fields, in a unique partnership-based approach and boosting skills training relevant for the future of Africa. Located in the municipality of Benguerir, in the very heart of the Green City, Mohammed VI Polytechnic University aspires to leave its mark nationally, continentally, and globally.

1. Context:

Effective characterization and modeling of groundwater resources are fundamental for sustainable water management in Morocco, particularly in the face of increasing water scarcity and environmental change. Traditional hydrogeological investigations can be time-consuming and spatially limited. This PhD project aims to develop a novel and integrated approach to groundwater assessment by synergistically combining Enhanced Data Acquisition and Monitoring using remote sensing and geophysics with Advanced Data Analysis and Modeling through hybrid Machine Learning/Deep Learning (ML/DL) and Numerical models. This research will lead to a more accurate and comprehensive understanding of groundwater systems in the Moroccan context, with particular focus on priority basins such as Souss-Massa, Tensift, and Sebou.

2. Research Objectives:

This PhD project aims to develop a novel and integrated approach to groundwater assessment by synergistically combining:

○ Enhanced Data Acquisition and Monitoring: Using remote sensing and geophysical techniques to characterize groundwater systems across diverse Moroccan hydrogeological settings.

○ Advanced Data Analysis: Through the implementation of Machine Learning and Deep Learning algorithms to predict key hydrogeological parameters from integrated datasets.

○ Hybrid Modeling Approaches: That combine ML/DL predictive capabilities with process-based numerical groundwater flow and reactive transport models.

○ High-Resolution Surface Water-Groundwater Integration: Through sophisticated numerical models that incorporate geological heterogeneity, coupled interactions, and climate drivers using High-Performance Computing resources.

○ Implementing Advanced Data Assimilation Techniques: Integrating real-time and historical data from enhanced monitoring networks (including IoT sensors, remote sensing, and potentially fiber optic sensing) into the numerical models using advanced data assimilation methods (e.g., Kalman filtering, ensemble Kalman filtering). This will lead to continuous model updates, improved state estimation, and reduced predictive uncertainty.

○ Reactive Transport Modeling: To characterize and predict geochemical processes and contaminant fate in groundwater systems, accounting for complex water-rock interactions, sorption phenomena, and biogeochemical reactions that affect groundwater quality.

○ Uncertainty Quantification: For different data sources and modeling methodologies to enhance result reliability.

○ Case Study Applications: Focused on priority groundwater basins in Morocco to address specific management challenges.

Expected Outcomes:

• Development of transferable methodologies for groundwater assessment applicable to various Moroccan regions
• Publication of research findings in high-impact peer-reviewed journals
• Creation of improved tools for sustainable water resource management in Morocco
• Contribution to IWRI/UM6P's broader water security research initiatives

3. Admission Criteria:

We are seeking a highly motivated and enthusiastic candidate with:

○ A Master's degree (or equivalent) in HydroInformatics, Hydrogeology, Geophysics, Remote Sensing, Environmental Science, Water Resources Engineering, Civil Engineering with a focus on water, Computer Science, Applied Mathematics, Data Science, Geochemistry, or a related field.

○ A strong background in mathematics, physics, numerical methods, and programming (preferably Python, MATLAB, R, or similar).

○ Experience with remote sensing data processing, geophysical data analysis, and/or machine learning/deep learning techniques is highly desirable.

○ Familiarity with numerical groundwater modeling software (e.g., MODFLOW, FEFLOW) and reactive transport modeling (e.g., MT3D, RT3D, PHT3D, PHREEQC, CrunchFlow) is a significant advantage.

○ Excellent analytical, problem-solving, and communication skills.

○ The ability to work independently and as part of a multidisciplinary research team.

Position Details:

• Duration: 3 to 4 years (full-time)
• Funding: This position is fully funded, including a monthly stipend, research expenses, and conference travel support
• Resources: Access to IWRI/UM6P's advanced computational facilities and field equipment

4. References:

Relevant literature for this research includes:

El Mezouary, L., et al. (2024). Contribution to advancing aquifer geometric mapping using machine learning and deep learning techniques: a case study of the AL Haouz-Mejjate aquifer, Marrakech, Morocco. Applied Water Science, 14(5), 102.

El Mezouary, L., et al. (2024). Machine Learning and Deep Learning Guided Assessment of Groundwater Reservoir Hydrodynamic Parameters: A Case Study of The El Haouz Aquifer. In E3S Web of Conferences (Vol. 489, p. 04005). EDP Sciences.

Sun, A. Y., et al. (2019). "Combining Physically Based Modeling and Deep Learning for Fusing Satellite-Derived and In Situ Hydrological Observations." Water Resources Research, 55(2), 1179-1196.

Engel, M., Mischel, S., Quanz, S., Frei, S., Radny, D., Voelpel, R., & Schmidt, A. (2024). Localizing and quantifying groundwater‐surface water interactions at different scales: A tracer approach at the River Moselle, Germany. Hydrological Processes, 38(5), e15118.

Gökçe, S., & Şengör, S. S. (2025). Reactive Transport Modeling of Uranium in Subsurface: Impact of Field-Scale Heterogeneity and Biogeochemical Dynamics. Water, 17(4), 514.

Taccari, M. L., Wang, H., Nuttall, J., Chen, X., & Jimack, P. K. (2024). Spatial-temporal graph neural networks for groundwater data. Scientific Reports, 14(1), 24564.

Closing of Job Description:

To inquire or submit applications, please contact Pr. Lhoussaine EL MEZOUARY (Lhoussaine.ELMEZOUARY@um6p.ma). Important documents for applying: A detailed CV, a cover letter describing research interests and motivation (max 2 pages), academic transcripts from Bachelor's and Master's degrees, and a brief research proposal related to the project objectives (max 3 pages). Applicants should also provide names and contact details of two academic referees who can provide letters of recommendation.

UM6P.