Ph.D. student opening in Sweden on Earth Observation, Data Science, and AI for poverty estimation

2026-06-15 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Science & Research — Mathematics & Computational Sciences, Environmental Science & Earth Systems, Social Sciences & Behavioral Studies · Depth: Expert, quick

Summary

Chalmers University's AI and Global Development Lab in Sweden is offering a Ph.D. position focused on Earth Observation, Data Science, and AI for poverty estimation. This role is situated within the Data Science and AI division of the Department of Computer Science and Engineering. The lab, led by Adel Daoud, integrates AI with Earth Observation to analyze human development dynamics, including poverty, conflict, and sustainability. Their research involves analyzing satellite imagery dating from 1984 to the present and utilizing AI search agent swarms for large-scale knowledge discovery. Candidates should possess a strong background in data science, computer science, deep learning, or statistics, with remote sensing experience and causal inference as beneficial additions. The application deadline for this position is June 20, 2026.

Key takeaway

For AI students or research scientists considering doctoral studies, this Chalmers Ph.D. opening applies advanced AI and Earth Observation to poverty estimation. You should investigate the AI and Global Development Lab's "Planetary Causal Inference" work and their use of satellite imagery from 1984. Consider applying by the June 20, 2026 deadline if your background aligns with data science, deep learning, or statistics.

Key insights

The AI & Global Development Lab uses AI and Earth Observation to analyze human development and poverty dynamics from 1984 onwards.

Principles

• Fusing AI with Earth Observation illuminates human development.
• Interdisciplinary teams enhance understanding of global issues.
• Planetary-scale data reconstructs historical development trajectories.

Method

The lab develops methods by analyzing satellite imagery from 1984, using AI search agent swarms, and other planetary-scale sources for knowledge discovery.

In practice

• Analyze satellite imagery for historical trends.
• Apply AI search agents for knowledge discovery.
• Investigate causal inference in global development.

Topics

• Earth Observation
• Poverty Estimation
• Causal Inference
• Deep Learning
• Satellite Imagery
• Global Development

Best for: AI Scientist, Research Scientist, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.