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IWRI - Postdoctoral Research in Assessment of Groundwater Drought with Machine Learning

Employer
MOHAMMED VI POLYTECHNIC UNIVERSITY
Location
Morocco (MA)
Closing date
7 Jun 2024

Job Details

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.

About IWRI:

International Water Research Institute (IWRI/UM6P) aims to rethink and adapt research, development, innovation and training to new paradigms to meet the future challenges of water and climate.

IWRI implements interdisciplinary and transdisciplinary education and research programs around issues related to Water and Climate, on the campus of the Mohammed VI Polytechnic University in Benguerir. The objectives are to conduct cutting-edge research where local themes are connected 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), Hydro-informatics, Climate Change and Adaptation Strategies.

Job description:

IWRI is seeking a highly skilled and motivated Postodoctoral Scientist with advanced expertise in the assessment of the impacts of climatic drought on groundwater resources in the context of climate change, using Machine Learning models.

Key duties:

  • Investigate the impact of anthropogenic activities, including agricultural water pumping and other human-induced alterations, as well as the effects of climate change on the intricate dynamics of the water cycle, particularly within the contexts of vulnerable arid regions.
  • Groundwater Exploration and Management: Machine learning can assist in mapping groundwater resources, predicting groundwater levels, and optimizing groundwater extraction strategies. By analyzing geological, hydrological, and anthropogenic factors, machine learning models can provide insights into groundwater dynamics.
  • Spatiotemporal Analysis: Machine learning techniques, such as spatial regression, clustering, and classification, can analyze spatiotemporal patterns in hydrological data to identify trends, anomalies, and relationships between different variables, aiding in decision-making processes.
  • Climate Change Impact Assessment to assess the impact of climate change on hydrological processes. By analyzing historical climate data and hydrological observations, future changes in precipitation patterns, temperature, evapotranspiration, and other relevant variables can be projected.
  • Watershed Management and Land Use Planning to support watershed management by analyzing land cover changes, soil erosion, and vegetation dynamics. By integrating hydrological models with land use data, machine learning enables stakeholders to assess the impact of land use planning decisions on water resources.
  • Hydrological Data Assimilation to integrate diverse sources of hydrological data, including satellite observations, ground-based measurements, and model simulations. This integration improves the accuracy of hydrological predictions and enhances our understanding of complex hydrological processes.
  • Contribute to the technical coordination of ongoing projects, as well as capacity strengthening and training.
  • Play an active role in supervising and providing guidance to IWRI's PhD students who are actively involved in hydrological modeling research.  

Qualifications:

  • PhD in Water Resources Science or a closely related field.
  • Strong background in machine learning applications in hydrology.
  • In-depth knowledge of water resource challenges specific to Morocco.
  • Effective communication skills in both English and French (both written and oral).
  • Demonstrated track record of scientific publications.
  • International experience and a demonstrated ability to collaborate with researchers at both national and international levels are highly regarded.
  • Experience in co-supervising students is important.

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