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.

The Grassland Sciences group at ETH Zurich, part of the Department of Environmental Systems Science, invites applications for a postdoctoral position focused on partitioning forest CO2 and water vapour fluxes. This research is embedded in the INETFLUX project, a collaboration between ETH Zurich, WSL (Dr. Roman Zweifel), and CzechGlobe, aiming to develop innovative technologies to disentangle carbon dioxide and evapotranspiration fluxes in forests. The project seeks to advance process- and system-understanding of biosphere-atmosphere greenhouse gas exchange, particularly in response to management and climate. The successful candidate will develop knowledge-guided machine learning approaches (including XGBoost and SHAP analyses) to partition net ecosystem CO2 fluxes and evapotranspiration into gross primary production, ecosystem respiration, transpiration, and evaporation. The research will leverage existing tree dendrometer and sap flow measurements, as well as stable isotopes in tree rings, to provide additional constraints. Forest sites are located in Switzerland and the Czech Republic, and the candidate will be responsible for one eddy-covariance flux station within the Swiss FluxNet. Key responsibilities include identifying environmental drivers and their temporal development to understand forest responses to climate and extreme events, compiling tree dendrometer and sap flow data, presenting results, publishing findings, and participating in a 3-month stage at CzechGlobe. The role also involves knowledge exchange and capacity building within the project, including workshops, training visits, and co-supervision of doctoral students. Applicants must hold a PhD or doctoral degree with a strong research background in micrometeorology, greenhouse gas exchange, tree and/or ecosystem physiology. Experience in observations, modelling, or statistical analyses is required, along with excellent skills in large data analyses and proficiency in English. A driver’s license is mandatory, and experience in knowledge exchange and student supervision is advantageous. The position is funded for up to three years, with salary and social benefits provided according to ETH Zurich rules. ETH Zurich offers numerous benefits, including public transport season tickets, car sharing, sports facilities, childcare, and attractive pension benefits. The university values diversity, sustainability, and an inclusive culture, promoting equality of opportunity and a climate-neutral future. Applications must be submitted online via the ETH Zurich application portal by 1 April 2026. Required documents include a letter of motivation, CV with publication list, transcripts of Bachelor's, Master's, and PhD/doctoral studies, and contact information for two referees. The envisaged starting date is 1 July 2026 or upon agreement. For further information about the Grassland Sciences group, visit the group website. Questions regarding the position can be directed to Prof. Dr. Nina Buchmann at [email protected] (no applications via email). ETH Zurich is a leading university in science and technology, renowned for excellent education, cutting-edge research, and direct transfer of new knowledge into society. With over 30,000 people from more than 120 countries, ETH Zurich fosters independent thinking and excellence, working together to address global challenges.

The Grassland Sciences Group at ETH Zürich is seeking a Postdoctoral Researcher to join the INETFLUX project, focusing on innovative technologies to partition carbon dioxide and evapotranspiration fluxes in forests. This international collaboration involves ETH Zürich, WSL (Switzerland), and CzechGlobe (Czech Republic), and aims to advance understanding of how forests respond to climate variability and extreme events through their carbon and water fluxes. The research will develop and validate new machine learning approaches (including XGBoost and SHAP) to partition net ecosystem exchange (NEE) into gross primary production (GPP) and ecosystem respiration, as well as evapotranspiration (ET) into transpiration and evaporation. The project leverages eddy covariance flux observations, tree dendrometer and sap flow measurements, stable isotope data, and multi-site European forest datasets. Key responsibilities include developing ML-based flux partitioning methods, identifying environmental drivers and forest responses to climate extremes, contributing to Swiss FluxNet measurements (with responsibility for one EC site), collaborating with international partners (including a 3-month research stay at CzechGlobe), and publishing scientific results. The position is based at ETH Zürich, Switzerland, with a start date of July 2026 (or by agreement) and a deadline for applications on 1 April 2026. Applicants should have a PhD in micrometeorology, greenhouse gas exchange, tree or ecosystem physiology, or related fields, with strong skills in large data analyses and English. Experience in knowledge exchange and student supervision is a plus. The position is funded for up to three years, with salary and benefits according to ETH Zürich rules, including public transport, sports, childcare, and pension benefits. To apply, submit your application online via the ETH Zürich portal, including a motivation letter, CV with publication list, transcripts, and contact information for two referees. For more information, contact Prof. Dr. Nina Buchmann ([email protected]). This is an excellent opportunity for researchers interested in combining environmental science, machine learning, and ecosystem research at a leading European institution.