Doctoral position in landscape ecology and geospatial analysis

The doctoral researcher will primarily work on Work Packages 1 and 2, focusing on assessing ecological change in depopulating rural landscapes. Identifying shrinking and growing rural landscapes globally using large-scale population datasets and spatial analysis. Developing and applying spatial-temporal indicators of landscape heterogeneity and ecological processes. Analysing long-term landscape transformations (composition and configuration) using multi-source geospatial datasets (including satellite remote sensing data). Working with large geospatial datasets and implementing scalable workflows. Collaborating closely with other project members (including a second doctoral student working on well-being trajectories). Presenting results at conferences and publishing in peer-reviewed journals.

The Chair of Planning of Landscape and Urban Systems (PLUS) at the Institute for Spatial and Landscape Development, ETH Zurich, is seeking a highly motivated doctoral researcher (100%) to join the project “DEPOPLAND: Drivers and trajectories of social-ecological change in depopulating rural landscapes”, funded by the Swiss National Science Foundation. DEPOPLAND is highly interdisciplinary, bringing together expertise from landscape ecology, physical and human geography, land system science, and computational linguistics. The project is carried out in collaboration with partners at ETH Zurich, the University of Zurich, and the Universities of Kassel and Göttingen.

• A Master’s degree in ecology, geography, environmental sciences, landscape planning, geomatics, or a related field

• Excellent understanding of landscape ecology concepts and metrics

• Strong background in spatial analysis and remote sensing

• Proven programming skills (e.g. Python or R)

• Experience working with large geospatial datasets

• Very good command of English (written and spoken) and strong teamwork skills

• Experience with Google Earth Engine or cloud-based geospatial platforms

• Familiarity with spatial time-series analysis

• Experience with landscape metrics or ecological modelling

• Knowledge of reproducible workflows and version control