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 Unit Forest & Soil Ecology assesses the impact of climate change and management on forests and their soils. The group Critical Zone dynamics is looking for a

You will work within the recently funded SNSF project RESPIRE ("Refining terrestrial carbon export via observations of the deep critical zone"). The deeper critical zone, where rocks, water and life interact, remains a "black box" when it comes to our understanding of the terrestrial carbon cycle, particularly because of the difficulties of gaining observational datasets from the subsurface. This project aims to understand the relationships between climate, ecosystem, and the geological subsurface, and how these affect carbon export into and out of the deep unsaturated zone. By establishing the first comprehensive terrestrial subsurface carbon budget, the project will provide insight into this critical, but poorly constrained carbon flux. You will carry out fieldwork at several long-term forest ecosystem research sites across Switzerland, where monitoring and sampling infrastructure will be installed to capture trends in environmental and geochemical signals in the deeper soil and at the transition to groundwater. The research will involve measuring carbon fluxes in fluid and gaseous phases using stable isotope and radiocarbon techniques and combining these measurements with near real-time monitoring and climate data from the sites. Your workplace will be at the Swiss Federal Research Institute WSL in Birmensdorf and you will be enrolled as a PhD student at ETH Zurich.

You hold a Master’s degree in environmental science, soil science, biogeochemistry or related disciplines. You possess strong communication skills, self-motivation, an aptitude for field research, strong quantitative skills, and a commitment to collaborative, interdisciplinary research. Coding experience and the use of statistical software (e.g., R) are of advantage. You also have good English writing and speaking skills.