Do you want to contribute to the sustainable management of natural hazards, working on multi-hazard risk science in a team of outstanding water and climate scientists? Please apply at Vrije Universiteit Amsterdam.
You will work on research leading to a PhD thesis on assessing the dynamics of multi-risk due to interactions between different natural hazards. Risk models have been developed to assess changes in risk in the past and future due to changes in hazard, exposure, and vulnerability. However, they examine long-term trends, assuming no interactions between risk drivers. Dynamic feedbacks are poorly represented, and we lack methods to assess and quantify changes in risk over time. This project aims to build an evidence and knowledge base of how multi-hazards influence dynamic feedbacks between risk drivers and thereby influence overall risk.
The position entails: (1) developing data-driven approaches for identifying empirical evidence of dynamics and feedbacks of risk drivers and past multi-risk interactions, (2) quantifying these dynamics and feedbacks, and (3) developing functions that account for interactions between multiple hazards and Disaster Risk Reduction measures. The research will contribute to the development of a database of feedbacks between risk drivers. Methods include advanced machine learning techniques, such as generative models or neural networks, and short- and long-term time-series analysis accounting for spatial and temporal dynamics.
This research is part of a large EU-funded project, MYRIAD-EU. The work will be carried out at the Institute for Environmental Studies (IVM) at VU Amsterdam. You will work in close collaboration with our consortium partners, including the Max Planck Institute, IIASA, Deltares, and CMCC. The results will be used to inform decision-making related to the Sustainable Development Goals and the Sendai Framework for Disaster Risk Reduction.
- collating empirical data on multi-risk
- quantifying and modelling multi-risk interactions
- writing a PhD thesis consisting of 4 scientific papers
- working with colleagues of the project consortium and contributing to project reporting
- contributing to the teaching activities of IVM
- MSc degree in econometrics, data science, computational sciences, or similar (preferably with demonstrable affinity with climate, earth or environmental sciences) or MSc degree in earth science-related subject (with demonstrable affinity with econometrics, data science, computational sciences, or similar)
- good skills in programming and using large spatial datasets (preferably in Python, or willing to learn Python). Experience with High Performance Computing (or interest in learning) is desirable
- familiar with or willing to learn Machine Learning techniques
- strong quantitative methodological skills, in particular knowledge of multivariate statistical methods
- The position involves collaboration and communication in a larger team of researchers and stakeholders from other disciplines involved in international research projects