About UM6P:
Mohammed VI University of Technology is an institution dedicated to research and innovation in Africa and aims to join among the world-renowned universities in the field.
The university is committed to economic and human development, putting research and innovation at the forefront of African development. A mechanism that allows Morocco to strengthen its front-line position in these areas through a unique partnership-based approach and enhanced skills training relevant to Africa's future.
Located in the heart of the Green City, Ben Gelir, Mohammed VI University of Technology aims to leave its mark on the country, the continent and the world.
About IWRI:
The International Water Research Institute (IWRI/UM6P) aims to rethink and adapt research, development, innovation and training to new paradigms to meet future water and climate challenges.
IWRI conducts interdisciplinary and interdisciplinary education and research programs focused on water and climate-related issues at the Mohammed VI University of Technology campus in Bengelir. The aim is to carry out cutting-edge research where regional themes relate to global issues of water and climate, with a focus on:
Integrated Water Resources Management (IWRM), development of advanced water technologies (irrigation, water supply and sanitation, desalination, wastewater treatment and reuse), hydroinformatics, climate change and adaptation strategies.
job description:
IWRI is seeking a highly skilled and motivated postdoctoral researcher with advanced expertise in assessing the impacts of climate drought on groundwater resources in the context of climate change using machine learning models.
Main duties:
- We investigate the effects of anthropogenic activities, including agricultural water extraction and other anthropogenic modifications, and the impact of climate change on the complex dynamics of the water cycle, particularly in vulnerable arid regions.
- Groundwater exploration and management: Machine learning helps map groundwater resources, predict groundwater levels, and optimize groundwater extraction strategies. By analyzing geological, hydrological, and anthropogenic factors, machine learning models can provide insight into groundwater dynamics.
- Spatio-temporal analysis: Machine learning techniques such as spatial regression, clustering, and classification analyze spatio-temporal patterns in hydrological data to identify trends, anomalies, and relationships between different variables to aid the decision-making process.
- Climate change impact assessment: Assessing the effects of climate change on hydrological processes. By analyzing historical climate data and hydrological observations, we can predict future changes in precipitation patterns, temperature, evapotranspiration, and other related variables.
- Watershed Management and Land Use Planning Support watershed management by analyzing land cover change, soil erosion, and vegetation dynamics. By integrating hydrological models and land use data, machine learning enables stakeholders to assess the impact of land use planning decisions on water resources.
- Hydrological data assimilation: Integrate diverse sources of hydrological data, including satellite observations, ground measurements, and model simulations. This integration improves the accuracy of hydrological predictions and deepens our understanding of complex hydrological processes.
- Contribute to technical coordination, capacity building and training for ongoing projects.
- You will play an active role in supervising and mentoring IWRI's doctoral students who are actively involved in hydrological modeling research.
Qualifications:
- PhD in water resources science or closely related field.
- Rich background in machine learning applications in hydrology.
- In-depth knowledge of Morocco's specific water resource challenges.
- Effective communication skills (both written and oral) in both English and French.
- Proven track record of scientific publications.
- International experience and proven ability to collaborate with national and international level researchers are highly valued.
- Experience co-teaching students is important.
