Doctoral student in Earth Observation, Data Science, and AI for poverty estimation Chalmers University of Technology in Sweden

Chalmers University of Technology invites applications for a fully funded doctoral student position in Earth Observation, Data Science, and AI for poverty estimation. This interdisciplinary project aims to develop deep-learning methods to estimate living conditions across Africa using satellite imagery, and to compare the effectiveness of different satellites for this purpose. The research will contribute to evaluating progress toward the Sustainable Development Goals in villages and cities across Africa and beyond.

The position is hosted by the AI and Global Development Lab at the Division of Data Science and AI (DSAI), Department of Computer Science and Engineering, Chalmers University of Technology, and the Institute for Analytical Sociology, Linköping University. The lab is internationally connected, with collaborators in Sweden, the United States, India, Chile, and the United Kingdom, and publishes in top journals and conferences. The project is funded by the Swedish National Space Agency and runs within the Observatory of Poverty programme.

The doctoral student will lead three work packages: (1) develop deep-learning models to estimate multidimensional poverty from Sentinel-2 satellite imagery; (2) benchmark models using Pléiades and Landsat satellites to identify optimal trade-offs between precision and computational cost; (3) apply AI explainability methods to interpret model predictions and build trust in earth-observation-based estimates for policy use. The student will also contribute to the ObservatoryOfPoverty open-source software, enabling the wider research community to produce and use poverty estimates for policy evaluation.

Mentorship is provided by Professor Adel Daoud (principal supervisor), Affiliated Associate Professor in Data Science and AI for the Social Sciences at Chalmers, and Professor of Computational Social Science at Linköping University, and Associate Professor Ashkan Panahi (secondary supervisor) in the Division of Data Science and AI at Chalmers. Additional mentors include senior lab members and international collaborators. The lab meets weekly, both in person and remotely, and offers a supportive, collaborative environment.

Applicants must have a Master’s degree (or equivalent) in computer science, data science, statistics, applied mathematics, electrical engineering, signal processing, physics, computational social science, or a related field. For students educated outside Sweden, a 4-year Bachelor’s degree is accepted. Strong English communication skills and solid programming skills in Python (or R, Julia, C++) are required, along with hands-on experience in deep-learning frameworks (PyTorch, TensorFlow, JAX). Foundational knowledge of deep learning and computer vision is essential. Experience with image processing, satellite imagery, geospatial libraries, remote sensing, spatial statistics, modeling geo-temporal data, causal inference, and statistical issues in prediction is advantageous. Independent research capacity demonstrated by thesis, publication, conference paper, or open-source contribution is valued. The project welcomes candidates from diverse backgrounds and encourages spin-off ideas aligned with the project objectives.

The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, extending the position to five years. The starting salary is 34,550 SEK per month (valid from May 25, 2025), and doctoral students are employees with full benefits. Physical presence in Gothenburg is required throughout the study period, and a valid residence permit must be presented by the start date. Chalmers offers a dynamic and inspiring working environment, generous parental leave, subsidised day care, free schools, healthcare, and Swedish language courses for non-native speakers. The university is committed to gender balance, equality, and inclusion.

To apply, prepare your application in English and attach as PDF files, including a comprehensive CV, personal letter, relevant theses or publications, and transcripts. Use the application form link provided; do not send applications by email. Ensure your application is complete, as incomplete submissions will not be considered. Finalists may be invited for an in-person interview at Chalmers. The application deadline is 13 June 2026. For questions, contact Professor Adel Daoud at [email protected].