The Swiss Federal Institute for Forest, Snow and Landscape Research WSL is part of the ETH Domain. Approximately 600 people work on the sustainable use and protection of the environment and on the handling of natural hazards.

The Research Group Dynamic Macroecology led by Dr. Niklaus E. Zimmermann studies questions related to spatial ecology, macroecology and -evolution and global biome dynamics. For this group, we are looking for the duration of 18 months (possibility of extension) for a

as part of an SNSF (ERC) Advanced Grant project (Predicting hotspots of climate change impacts on ecosystems from an eco-evolutionary biome framework). This project aims at mapping global biomes, projecting global change impacts on biomes, and testing a novel eco-evolutionary biome theory. In this project, you contribute to characterizing global biomes based on field measurements and existing data. Specifically, you will: 1) contribute to sampling drone-based (LiDAR, multispec) and ground-based (hemispherical photos, LAI 2200) measurements in the field for different biomes and compile existing data from large databases; 2) analyze these data for systematic differences in vegetation structure; 3) refine computer vision models to extract leaf traits and other plant functional characteristics from photos from above and below the biome canopies; and 4) analyze global databases of plant photos to extract functional properties, using the trained computer vision models. Ultimately, you will contribute to characterizing functional and structural properties of biomes and to upscaling this information globally, using information on plant distributions. You will work in a dynamic research group at the Swiss Federal Research Institute WSL in Birmensdorf, Switzerland, and you will collaborate with international partners.

You hold a PhD degree in ecology or a related field, and you have strong skills in macroecology, functional ecology, the processing and analysis of high-volume and remote sensing data (especially LiDAR and multispec), spatial modelling, and in coding with R/Python/GIT. Expertise in calibrating deep learning tools, cluster computing and/or fieldwork are beneficial. The capability to develop new ideas independently is necessary, as are very good English skills (both written and oral). You are a curious, self-reliant person who is also an excellent team worker and values cooperation.