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.

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 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

• 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