PhD position: Geomorphic hazards and compound events in Africa


The Royal Museum for Central Africa (RMCA) and the Vrije Universiteit Brussel (VUB) seek for a motivated candidate for a new PhD position. The successful candidate will be advised by Dr. Olivier Dewitte and Dr. François Kervyn from RMCA and Prof. Dr. Wim Thiery from VUB. The research will be carried out in close collaboration with Dr. Nicolas d’Oreye (ECGS/MNHN, Luxembourg). Wide opportunities for collaboration exist with research institutions in close proximity.

The research group GeoRisKA of RMCA has its research activities in the fields of geology, geomorphology, natural hazards and risk assessment. Most of its study areas are located in Central Africa. Remote sensing, GIS, and field work are used to support the research as well as for assisting in thematic mapping. The BCLIMATE group at VUB employs global climate modelling, land surface modelling, field observations and data analysis to study climate change and extreme events (notably extreme precipitation and heatwaves) and has recently started a new research line on compound events.

Project description

Geomorphic hazards such as landslides and flash floods often result from a combination of interacting physical and anthropogenic processes across multiple spatial and temporal scales. The combination of processes (drivers and hazards) leading to a significant impact is referred to as a ‘compound event’. This research aims to unravel the climate, earth and landscape signatures in the patterns of geomorphic hazards in tropical climates and assess the timing of the related compound events. The region of interest is the western branch of the East African Rift, a region of various tropical climates prone to geomorphic hazards where environment changes are important.

Key to this research project will be the development of an unprecedented inventory of geomorphic hazards with accurate detection and timing. This will be done by developing a method that combines radar and optical open-access satellite remote sensing adapted for frequently cloud-covered climates. The method will be validated against citizen-based field information. Machine learning methods will be used for both the remote sensing part and the analysis of the patterns of the hazards.

More information

For more information about responsibilities, qualifications and how to apply, please visit this link.