PhD Candidate Geospatial Risk Modelling for Climate Finance

Effectively understanding and mitigating financial risks associated with climate change is important for sustainable economic decision-making. Climate finance is critical in addressing physical risks by quantifying economic losses from climate-related disasters and developing financial instruments that enhance market resilience. As global financial markets increasingly recognise the importance of climate risks, central banks and regulatory bodies demand improved assessments of the disaster risk of local assets and supply chains.

Advancements in geospatial analytics, climate modelling, and financial risk assessment now allow a more precise evaluation of climate-induced risks. Integrating these disciplines enables a deeper understanding of sectoral and global financial vulnerabilities and supports the development of more effective risk management strategies.

Despite growing attention to climate-related financial risks, significant knowledge gaps remain, particularly in the availability and granularity of asset-level data. Many financial institutions lack precise information on the physical location of their assets or those of the companies they invest in, which limits the ability to assess exposure to localized hazards. This challenge is compounded by the difficulty of linking geospatial data with asset ownership, making it hard to evaluate how physical risks translate into financial vulnerabilities at the institutional level.

This PhD research aims to develop a holistic modelling framework for assessing financial risks associated with climate-related hazards at the granularity of asset-level data. Therefore, you will be integrating geospatial data with financial and climate information. You will establish a new protocol for linking asset location to ownership information and for quantifying potential financial losses from various climate-related disasters, the latter being considered both individually or as multi–hazardous events. As such, you will contribute to more accurate financial risk assessments of extreme events linked to climate change, equipping investors, policymakers, and financial institutions with actionable insights for informed decision-making.

• An MSc degree in quantitative sciences such as geo-information science, geomatics engineering, or economics or a related field
• Interest in spatiotemporal modelling
• Proficiency in scientific computing (Python or R)
• Good communication skills and an excellent command of English. Fluency in Dutch or willingness to learn Dutch is a plus
• Familiarity with financial data analysis and remote sensing is considered an advantage

Interested and motivated candidates are encouraged to apply, even when they still need to possess all desired skills. You can develop relevant skills on the job through dedicated learning and doctoral training.

The Department of Applied Earth Sciences combines earth scientific knowledge with dynamic modelling and advanced remote sensing, to analyse earth systems and processes in space and time. Our goal is to contribute to global challenges concerning future demands for earth resources and to help reduce disaster risk and the impact of natural hazards on communities living in changing environments.