Doctoral Student in Isotope-Enabled Evapotranspiration Partitioning ETH Zürich in Switzerland

This fully funded PhD position at ETH Zurich is part of the IsoFlux project, focusing on isotope-enabled flux partitioning of evapotranspiration (ET) in Swiss forest and grassland ecosystems. Hosted by the Grassland Sciences and WaldLab Ecohydrology groups, the research aims to advance understanding of biosphere–atmosphere greenhouse gas exchange, ecohydrology, and stable water isotope applications. The project investigates ecosystem water and energy fluxes, ecosystem resilience to climate extremes, and develops robust methods to partition ET into evaporation and transpiration using high-frequency water vapor isotope measurements and eddy-covariance fluxes.

The doctoral student will deploy a mobile in-situ isotope measurement system across five Swiss FluxNet sites, conduct fieldwork including soil and plant sampling, and perform water extractions to identify water sources. The role involves statistical analyses and machine learning to compare isotope- and flux-based ET partitioning and to identify drivers of ET, E, and T across seasons and years. The position offers a vibrant, international research environment with strong scientific and technical support from both groups.

Applicants must hold a Master’s degree in atmospheric sciences, environmental sciences, forest sciences, hydrology, ecology, or a closely related field, with experience in stable water isotopes, micrometeorology, biogeochemistry, and/or plant ecophysiology. Fieldwork experience, strong statistical and programming skills (R or Python), proficiency in English, and a driver’s license are required. The position is funded for four years with salary and social benefits according to ETH Zurich rules, supported by an ETH Research Grant.

ETH Zurich is committed to diversity, sustainability, and providing a supportive environment for professional development. The application deadline is 1 February 2026, with the earliest start date of 1 April 2026. Applications must be submitted online and include a motivation letter, CV, academic transcripts, and contact information for two referees. For further information, contact Dr. Marius Floriancic or Prof. Dr. Nina Buchmann.