To continue strong leadership of the GLOBIO project, we are looking for a candidate with an impressive academic background in ecological modelling for policy applications, alongside experience in leading and managing research projects. GLOBIO is a global model to assess large-scale impacts of human changes in the environment (e.g., climate change, land use, pollution) on terrestrial and freshwater ecosystems (including biodiversity and ecosystem services). GLOBIO is also part of PBL's IMAGE-GLOBIO framework: a comprehensive integrated modelling framework of interacting human and natural systems. GLOBIO is consistently used in integrated biodiversity assessments that include trade-offs and co-benefits between nature, nature’s contributions to people (ecosystem services) and other sustainable development goals. In this position you will lead and contribute to the further development and application of the global biodiversity model GLOBIO.
This includes the following tasks:
- Leading the GLOBIO project including defining, planning and coordinating further model development activities in line with the GLOBIO strategy.
- Coordinating the GLOBIO project work at PBL and in close collaboration with several university partners.
- Translate applied policy questions to needs and actions for further model development (e.g., relations between biodiversity, ecosystem services and various environmental pressures such as climate change, land use, pollution, and feedbacks from nature to society), in collaboration with the IMAGE team.
- Contribute to the application of GLOBIO in (e.g. Horizon) projects on scenario simulations to inform biodiversity-relevant policy processes (e.g., via CBD, IPBES) in collaboration with (inter)national research institutes.
- Contribute to the technical implementation of novel model components.
- Contribute to acquisition / proposal writing for GLOBIO relevant tenders, when relevant in collaboration with the IMAGE team.
- Fostering scientific excellence and ensuring model quality, including through publishing model improvements in academic literature.