PhD in Computational Ecosystem Science: Water-carbon coupling
Our group for Computational Ecosystem Science develops new model and data integration methods to gain a mechanistic understanding of biogeochemical cycling in ecosystems, water-carbon coupling, and forest responses to climate change. We are co-hosted at the ETH Institute of Agricultural Sciences and the WSL Unit Forest Dynamics and work at the intersection of Earth system science, ecophysiology, ecology, applied statistics, and high-performance computing. Within the Swiss National Science Foundation project MIND – Next-generation modelling of the biosphere: Including new data streams and optimality approaches, we are recruiting for as soon as possible a PhD in Computational Ecosystem Science: Water-carbon coupling.
You will investigate water-carbon coupling in terrestrial ecosystems and to develop new methods for predicting and monitoring plant water stress effects. How much water plants have access to, determines climate impacts on vegetation and is a key regulator of land surface temperatures. Predicting how plant architecture, groundwater, and CO2 affects vegetation sensitivity to drought will be your aim. To do so, you will develop methods (machine learning and process-based modelling) and combine these with large datasets from ecosystem flux measurements, remote sensing, and ground-based observations. The project may also include the collection of field data.
This position requires independent and creative thinking to formulate hypotheses; to critically assess the science at the intersection of ecophysiology, biogeochemistry, ecology, hydrology, and micro-meteorology; develop novel modelling methods; and to address a high-profile research challenge for a better understanding of global environmental change and climate impacts.
The candidate must hold a M.Sc. degree in natural sciences, mathematics, or engineering and should demonstrate proficient English written and oral skills and excellent analytical and numerical skills. Experience with programming and other data science methods are an asset and an open and collaborative mentality for the development of our computational infrastructure is expected.
We offer the candidate to be part of a small group with a strong collaborative philosophy and to benefit from a world-leading academic environment, from an international collaboration within the project and from the excellent quality of life in Switzerland.
We look forward to receiving your online application including the following documents: Motivation letter (max. 2 pages, with a statement of research interests), a CV, copies of academic qualifications and the names and e-mail addresses of three referees. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
For further information about the project please visit the website stineb.github.io/mind. Questions regarding the position should be directed to Prof. B. Stocker by email: firstname.lastname@example.org (no applications) or phone +41 44 632 48 90.