Nina Buchmann

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.

The Swiss Data Science Center (SDSC) at ETH Zürich is offering a postdoctoral position as part of the PhenoMix project, funded by the Swiss National Science Foundation (SNSF). This interdisciplinary project focuses on developing advanced machine learning and computer vision methods for automated plant phenotyping, with a strong emphasis on sustainable agriculture and crop science. The successful candidate will join a collaborative team spanning SDSC, the Crop Science Group (Prof. Achim Walter), the Grassland Sciences Group (Prof. Nina Buchmann), and AGROSCOPE (Dr. Susanne Vogelgsang), working at the intersection of data science, agricultural sciences, and environmental systems. The PhenoMix project leverages the Field Imaging Platform (FIP), a high-throughput phenotyping facility, and field experiments to generate multi-modal datasets of pure stands and crop mixtures. The postdoctoral researcher will develop novel data science tools for automated trait estimation, including foundation models for phenotyping, domain transfer methods, 3D reconstruction and rendering, human-in-the-loop approaches, and rigorous field evaluation. The role involves creating models that generalize across imaging platforms and environmental conditions, with real-world impact for farmers, breeders, and researchers. Key responsibilities include designing and implementing machine learning approaches for multi-trait plant phenotyping, developing domain-specific and physiologically plausible models, deploying active learning strategies, conducting field experiments, and generating comprehensive datasets for downstream analyses. The postdoc will also contribute to codebases, engage with open source communities, supervise students, and prepare scientific publications for top-tier venues. Applicants must have a PhD in computer science, machine learning, data science, or a relevant domain science, with demonstrated expertise in machine learning and computer vision. Required skills include proficiency in deep learning frameworks (PyTorch preferred), scientific programming in Python, experience with large multi-modal datasets, and excellent communication skills in English. Beneficial competencies include 3D reconstruction, active learning, Bayesian optimisation, and familiarity with agricultural sciences. The position is fully funded for up to 4 years, offering access to state-of-the-art phenotyping infrastructure, computational resources, and opportunities for professional development, including publishing research, presenting at international events, and supervising MSc and BSc students. ETH Zürich values diversity, sustainability, and work-life balance, providing a stimulating and inclusive research environment in Zurich. Applications must be submitted online via the ETH Zurich application portal. Required documents include a letter of motivation, CV with publication list, academic diplomas, transcripts, certificates, and contact details for 2–3 references. Links to code repositories or portfolios may be included. For questions regarding the position, contact Dr. Michele Volpi ([email protected]). Applications via email or postal services will not be considered. ETH Zürich is a world-leading university specializing in science and technology, renowned for its excellent education, cutting-edge research, and commitment to solving global challenges. The PhenoMix project offers a unique opportunity to contribute to the advancement of automated plant phenotyping and sustainable agriculture through innovative machine learning research.

ETH Zürich is offering two fully funded PhD positions in plant ecophysiology and ecosystem services of crop mixtures as part of the PhenoMix project, supported by the Swiss National Science Foundation. The PhenoMix project investigates agroecologically beneficial legume-cereal crop mixtures, focusing on multidimensional field phenotyping. The successful candidates will join the Grassland Sciences group within the Department of Environmental Systems Science, a vibrant and international research team specializing in biosphere-atmosphere greenhouse gas exchange, plant ecophysiology, and ecosystem services of agroecosystems. The doctoral projects will assess differences in canopy microclimate, plant traits, and ecosystem services between pure stands and crop mixtures. Using stable carbon, nitrogen, and water isotope applications, the research will determine light, nitrogen, and water use, and evaluate complementarity in crop mixtures over three cropping seasons. Tasks include measurements of canopy microclimate and architecture, plant traits, biomass production, soil resource supply, and plant sampling for stable isotope analyses. Additional responsibilities involve quantifying litter decomposition, leaching, weed pressure, and yield quality, as well as evaluating plant phenotyping models and conducting statistical analyses, including machine learning approaches. The positions also require assistance in plot establishment, maintenance, and harvest across all PhenoMix work packages. Both positions are full-time and funded for four years, with salary and social benefits provided according to ETH Zurich rules. Applicants must have a Master’s degree in agricultural sciences, biology, environmental sciences, ecology, or a closely related field, with experience in plant ecophysiology, soil science, community ecology, and/or stable isotopes. Fieldwork experience, strong statistical skills, and programming proficiency (e.g., R or Python) are essential. A good standard of written and spoken English and a driver’s license are mandatory. Candidates should demonstrate good writing skills and the ability to work collaboratively in an interdisciplinary team. ETH Zurich offers numerous benefits, including public transport season tickets, car sharing, sports facilities, childcare, and attractive pension plans. The university values diversity, sustainability, and an inclusive culture, promoting equality of opportunity and nurturing a fair and open environment for all staff and students. The desired starting date is 1 August 2026, and interviews will begin by mid-May 2026. Applications must be submitted online by 15 May 2026, including a letter of motivation, CV, academic certificates and transcripts, and contact information for two referees. For further information, contact Prof. Dr. Nina Buchmann at [email protected]. Applications via email or postal services will not be considered. ETH Zurich is renowned for its excellence in education, fundamental research, and societal impact, with over 30,000 people from more than 120 countries. The university is committed to developing solutions for global challenges and fostering independent thinking and excellence.

The Swiss Data Science Center (SDSC) at ETH Zurich, in collaboration with the Crop Science Group, Grassland Sciences Group, and AGROSCOPE, invites applications for a postdoctoral position in the PhenoMix project, funded by the Swiss National Science Foundation (SNSF). This interdisciplinary project focuses on developing advanced machine learning and computer vision methods for automated plant phenotyping, with a strong emphasis on sustainable agriculture and crop science. The successful candidate will join a vibrant, multidisciplinary team at the SDSC Zurich office, working closely with leading experts including Prof. Achim Walter (Crop Science Group), Prof. Nina Buchmann (Grassland Sciences Group), and Dr. Susanne Vogelgsang (AGROSCOPE). The project leverages the Field Imaging Platform (FIP), a state-of-the-art high-throughput phenotyping facility, to generate and analyze multi-modal datasets from pure stands and crop mixtures. The postdoc will develop novel data science tools for automated processing of image time series, plant trait information, and 3D reconstructions, bridging advanced machine learning with practical agricultural applications. Key responsibilities include designing and implementing foundation model-based approaches for multi-trait plant phenotyping, developing domain transfer and adaptation methods, creating 3D point clouds and neural renderings, and deploying human-in-the-loop and active learning strategies. The role also involves conducting field experiments, evaluating model performance, and generating comprehensive datasets for downstream analyses. The postdoc will contribute to open-source codebases, supervise students, and present research at top-tier conferences and workshops. Applicants must have a PhD in computer science, machine learning, data science, or a related field, with demonstrated expertise in machine learning and computer vision. Strong programming skills in Python and experience with deep learning frameworks (preferably PyTorch) are essential. Additional desirable skills include experience with 3D reconstruction, active learning, and familiarity with agricultural sciences or plant phenotyping. Excellent communication skills and a collaborative mindset are required. The position offers up to 4 years of SNSF funding, a stimulating and diverse research environment, access to cutting-edge infrastructure and computational resources, and opportunities for professional development and international collaboration. ETH Zurich values diversity, sustainability, and work-life balance, providing an inclusive and supportive environment for all staff and students. To apply, submit your application via the ETH Zurich online portal, including a motivation letter, CV with publication list, academic transcripts, references, and code portfolio links if available. For further information, contact Dr. Michele Volpi ([email protected]). Applications via email or postal services will not be considered.