PhD Student (f/m/d) in AI & Sustainability Policy Lab Interdisciplinary Transformation University (IT:U) in Austria

IT:U (Interdisciplinary Transformation University) in Linz, Austria is advertising a 4-year PhD position in the AI & Sustainability Policy Lab within the IT:U Doctoral School / PhD Program Computational X. The project is led by Professor Elisabeth Gsottbauer and focuses on using AI- and data-driven methods to design, evaluate, and scale effective climate and conservation policies.

The research agenda combines causal inference, behavioral and experimental economics, econometrics, machine learning, and policy analysis. The PhD student will work on questions related to the environmental and socio-economic impacts of climate and conservation policies, policy targeting and prediction, heterogeneous treatment effects, and the integration of econometrics with AI for transparent evidence-based decision-making. The position emphasizes causal policy evaluation using field and survey experiments, quasi-experimental designs, and computational methods.

This is a structured doctoral program with a strong interdisciplinary component. In the first year, students take research lab modules and Project Integrated Courses (PICS) and complete a PhD Proposal Presentation. The remaining years are dedicated to thesis development, interdisciplinary seminars, and project assistant work. The lab also supports collaboration with international partners in academia, policy institutions, and sustainability organizations, as well as interdisciplinary co-supervision.

The post offers an on-site position in Linz, 30 hours per week, starting in October 2026, with a gross salary aligned with the FWF salary rate of EUR 2,832.10. The lab encourages international research exchanges, conference participation, mentoring in publication strategy, grant writing, and science-to-policy translation. Optional supplementary teaching or research contracts may be discussed during recruitment.

Applicants should hold a Master’s-equivalent degree in economics, data science, statistics, public policy, or a related field, and bring strong quantitative skills, programming ability in Stata/R/Python, empirical research experience, and fluency in English. Preferred backgrounds include applied econometrics, experimental or behavioral economics, causal inference, and policy evaluation, with additional strengths in geospatial/text data and causal machine learning. The deadline is 5 August 2026, and applications must be submitted online with the required academic documents and referee contacts.