Field of Research & Scope
The AI & Sustainability Policy Lab make use of AI- and data-driven methods to design, evaluate, and scale effective climate and conservation policies. We combine causal inference, behavioral/experimental economics, and machine learning to support evidence-based sustainability transitions. Climate change and biodiversity loss pose complex societal challenges that require both rigorous policy evaluation and advanced computational tools. In our lab, we are interested in
(1) generating credible causal evidence on the environmental and socio-economic impacts of climate and conservation policies;
(2) the development and application of machine learning methods to improve policy targeting, prediction, and heterogeneous treatment effect estimation; and
(3) the integration of econometrics and AI to build transparent tools that support adaptive, evidence-based decision-making.
During their stay, the PhD student will work collaboratively within a shared research agenda while pursuing complementary but independent topics. The position will focus primarily on causal policy evaluation using econometric methods and insights from behavioral science, field and survey experiments, and quasi-experimental research designs. Research may involve theory, modeling, surveys, experiments, quasi-experimental designs, and computational methods, often in international, interdisciplinary, and transdisciplinary collaboration.
Program Structure
IT:U offers a 4-year, structured PhD program.
In the first year, emphasis is placed on focused group work, research lab modules, and Project Integrated Courses (PICS). The first year concludes with a PhD Proposal Presentation. Over the next three years, students will develop and write their PhD theses. This is accompanied by interdisciplinary research seminars and work as a project assistant. The PhD program concludes after the 4th year with the submission and defense of the PhD thesis.
We offer
- Collaborations: The lab collaborates with international partners in academia, policy institutions, and sustainability organizations. The lab actively supports interdisciplinary co-supervision within IT:U and with external partners.
- International mobility and exchange: Candidates will benefit from international research exchanges with collaborating institutions across Europe and beyond. Short-term research stays and participation in international conferences are encouraged and supported.
- Professional and personal development: Doctoral students will receive structured mentoring in research design, publication strategy, grant writing, and science-to-policy translation. The lab emphasizes academic excellence, policy relevance, and the development of transferable skills for careers in academia, public institutions, or the private sector.
Responsibilities & Tasks
- conducting research
- writing academic publications
- attending national and international conferences
- contributing to activities of the research group
- collaborating with other research groups on an interdisciplinary basis
- performing auxiliary activities in academic teaching for up to 2 weekly credit hours
- helping to file applications for third-party funding
- complying with the institution’s academic standards and ethical guidelines
Skills & Qualifications needed
We seek highly motivated candidates with a strong interest in the intersection of sustainability, policy, and computational methods.
Applicants should have:
- A degree equivalent to a Master’s degree in economics, data science, statistics, public policy, or a related field
- Strong quantitative and analytical skills
- Proficiency in programming (e.g. Stata, R or Python)
- Experience with empirical research (e.g., through a thesis or research project)
- The ability to work independently and collaboratively in an interdisciplinary environment
- Fluency in English
Depending on profile, the following expertise is expected:
- Advanced knowledge of applied econometrics and experimental/behavioral economics
- Experience with causal inference methods (e.g., RCTs, DiD, IV, panel models)
- Interest in policy design and evaluation
Strong assets include:
- Experience with large-scale, geospatial, or text data
- Familiarity with causal machine learning
- Demonstrated interest in climate change, biodiversity, or sustainability policy
How to apply
Please fill in the online application form and upload your files at:
- Curriculum Vitae
- Bachelor’s and Master’s diploma (or equivalent)
- Bachelor's and Master’s Transcript of Records
- Motivational letter (max. 2 pages)
- Up to 3 contacts of professors/collaborators for recommendations
We look forward to receiving your online application.
Application deadline: August 5, 2026
