Field of Research & Scope

The AI & Sustainability Policy Lab develops AI- and data-driven methods to design, evaluate, and scale effective climate and conservation policies. We combine causal inference, behavioral 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 two PhD students will work collaboratively within a shared research agenda while pursuing complementary but independent topics. One position will focus primarily on causal policy evaluation using econometric methods, field and survey experiments, and quasi-experimental research designs. The other will focus on machine learning and AI methods for policy analysis, including causal machine learning, predictive modeling, and large-scale data integration. Research may involve theory, modeling, surveys, experiments, quasi-experimental designs, and computational methods, often in international, interdisciplinary, and transdisciplinary collaboration. 

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

• Conduct research 
• Write and publish scientific papers
• Attend national and international conferences
• Collaborate with other research groups on an interdisciplinary basis
• Contribute to academic teaching (up to 2 weekly credit hours)
• Support the research group in preparing applications for third-party funding
• Comply 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, computer science, statistics, public policy, or a related field
• Strong quantitative and analytical skills
• Proficiency in programming (R or Python; Stata is an advantage for economics-focused applicants)
• 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 (CEFR C1 or equivalent).

Depending on profile, the following expertise is expected: 
 
For the economics-focused position:

• 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 
   

For the AI-focused position: 

• Strong background in machine learning and statistical modeling 
  Experience with ML frameworks such as PyTorch or TensorFlow
• Solid understanding of statistical learning and model evaluation

Strong assets for both profiles include:

• Experience with large-scale, geospatial, or text data
• Familiarity with causal machine learning
• Demonstrated interest in climate change, biodiversity, or sustainability policy 

What you can expect

• Innovative and stimulating working conditions in an interdisciplinary, international research environment.
• Office kitchen with complimentary basic supplies.
• Austrian “KlimaTicket OÖ” (unlimited travel on all public transportation within  Upper Austria).
• We offer a gross salary in line with the FWF of EUR 2.832,10 on a 30h basis 
• Optional supplementary contracts (teaching or research) to an extent of up to 10 hours may be discussed during the hiring process.

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. 
 
Click here to view the curriculum. 

How to apply

Please fill in the online application form and upload your files at: https://apply.it-u.at/

• Curriculum Vitae
• Bachelor’s and master’s diploma (or equivalent)
• Bachelor’s and master’s transcript of records
• Motivational letter (2 pages max.)
• Up to 3 contacts of professors/collaborators for recommendations

We look forward to receiving your online application.

The positions will remain open until filled. Candidates are encouraged to apply early, as review of applications will begin on a rolling basis. The call will close on 30.04.2026.