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:
The International Water Research Institute (IWRI) provides research, education, and innovation in the fields of water and climate. In order to face the next challenges. IWRI seeks to rethink and adapt research development, innovation, and training to new paradigms. It is an institute that outlines forward-thinking pathways to address water issues in a systemic manner in Africa.
IWRI's vision is to Lead and develop integrated research and processes for Water & Climate concerns in Africa. We act as an African water hub through strategic cooperation and partnerships. We share comprehensive and diverse expertise to provide scientific information by focusing on four pillars: Integrated water resources management, Climate change and adaptation, Hydroinformatics, and Water technologies.
Position Summary:
The International Water Research Institute (IWRI) of the College of Agriculture and Environmental Sciences (CAES) at UM6P invites applications for a postdoctoral researcher in climate modeling and uncertainty quantification, focusing on extreme precipitation in data-scarce regions, with Morocco as a primary case study.
Extreme precipitation events are inherently rare and poorly observed, particularly in regions with sparse and heterogeneous monitoring networks. Climate models therefore play a central role in understanding and projecting such extremes, yet they introduce multiple sources of uncertainty that propagate across scales. This project aims to systematically quantify and interpret these uncertainties across the hierarchy of climate models, from global to convection-permitting scales.
Scientific context:
Climate projections rely on a hierarchy of models with increasing spatial resolution, each introducing distinct sources of uncertainty that affect the simulation of extreme precipitation. Global Climate Models (GCMs) provide physically consistent representations of large-scale atmospheric circulation and climate variability, but their coarse resolution limits their ability to resolve key processes such as orographic forcing and mesoscale convection. Regional Climate Models (RCMs) dynamically downscale GCM outputs to finer resolutions, improving the representation of topography and land–atmosphere interactions, yet they remain strongly dependent on boundary conditions and continue to rely on parameterized convection, which is a major source of error in extreme precipitation. Convection-permitting models (CPMs), operating at kilometer-scale resolution, explicitly resolve deep convection and have demonstrated improved skill in simulating short-duration and high-intensity precipitation events; however, their high computational cost limits ensemble sizes, and their uncertainty structure remains insufficiently explored, particularly due to sensitivities to boundary forcing and physical parameterizations. Understanding how uncertainty propagates across the GCM–RCM–CPM modeling chain is therefore a central challenge, especially in regions such as Morocco, where complex topography (Atlas Mountains, Rif Plateau), diverse climatic influences, and limited observational data combine to produce highly variable and poorly constrained extreme precipitation patterns.
Research Scope:
The postdoctoral researcher will investigate uncertainty propagation across multi-scale climate modeling frameworks by combining multi-model datasets (e.g., CMIP6, MIT Regional Climate Model simulations, and convection-permitting simulations) with dynamical and statistical downscaling approaches, extreme value theory, and spatial statistics. The work will focus on decomposing uncertainty into its key components while assessing the added value of high-resolution convection-permitting models for extreme precipitation. Particular attention will be given to observational uncertainty in sparse data environments through the integration of station, satellite, and reanalysis datasets. The project will also develop and apply advanced uncertainty quantification methods with the objective of providing robust guidelines for climate modeling of extremes in data-scarce and topographically complex regions.
Candidate Profile:
- PhD in climate science, atmospheric physics, applied mathematics, or related field
- Expertise in climate modeling and/or uncertainty quantification
- Strong programming skills (Python, MATLAB, Fortran, or similar)
- Experience with climate datasets and/or extreme event analysis is desirable
- Proven ability to handle HPC environments is an added asset
