Doctoral position in social indicator development with text-based and spatial data

The doctoral researcher will primarily work on Work Packages 1 and 3, focusing on modelling and analysing trajectories of objective and subjective well-being using spatial and computational text analyses.

• Identifying shrinking and growing rural landscapes globally using large-scale population datasets and spatial analysis
• Compiling, harmonising, and analysing region-specific time-series of objective and subjective well-being indicators
• Developing and applying computational text analysis workflows to derive spatially and temporally explicit indicators of subjective well-being from large text-based datasets (e.g. global news databases such as GDELT or other news archives)
• Designing and implementing machine learning models for analysing and predicting well-being indicators
• Collaborating closely with other project members (including a second doctoral student working on ecological trajectories)
• Presenting results at conferences and publishing in peer-reviewed journals

Do you have experience in Research?, Do you have a Master's degree?, 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.here, * A Master's degree in geography, social sciences, landscape planning, economics, data science, or a related field

• Strong interest in well-being research, social indicators, or human-environment interactions
• Experience with computational analysis of text data (e.g. natural language processing, text mining, or computational social science)
• Experience with spatial data analysis and programming (e.g. Python or R)
• Excellent command of English (written and spoken) and strong teamwork skills

Additional assets:

• Experience working with large-scale datasets (e.g. text corpora, event databases, or geospatial data)
• Experience working with survey data or social indicator datasets
• Familiarity with statistical or machine learning methods
• Knowledge of reproducible research practices and version control

• A stimulating, interdisciplinary research environment in a dynamic and international team
• Close collaboration with leading experts in social-ecological systems, computational text analysis, and well-being research
• Excellent working conditions at ETH Zurich, one of the world's leading universities
• Opportunities for academic development, publication, and international networking, * Cover letter (1 page) briefly describing your motivation, research interests, and how you meet each of the required and additional qualifications
• Curriculum vitae
• Academic transcripts and certificates
• Contact details of two referees
• Work examples (e.g. MSc thesis, code repositories, publications; if available)
• Testimonials (if available)

Further information about [Department/Institute] can be found on our [Website, create a hyperlink with Ctrl+k]. Questions regarding the position should be directed to Dr. Maarten J. van Strien, vanstrien@ethz.ch(no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

The position is for four years, with an intended start date on 1 November 2026.

Rural depopulation is emerging as one of the defining global transformations of the 21st century, with profound consequences for both ecosystems and human well-being. The DEPOPLAND project aims to provide the first systematic, global-scale analysis of ecological and social dynamics in depopulating rural landscapes, identifying trajectories of change and their underlying drivers. To do so, the project pioneers an integrated and data-driven approach to analysing social-ecological systems, combining diverse data sources and advanced computational methods to uncover long-term dynamics of ecosystems and human well-being. 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.