Assistant Professor, non-tenure track
- Vienna University of Economics and Business
- Research, development, teaching
- Fulltime
- published till: 2026-07-22
Department of Business Analytics and Decision Sciences
Fulltime, 40 hours/week
Starting October 15, 2026, and ending after 3 years
What to expect
- Conduct cutting-edge research: You will investigate large language models as tools, measurement instruments, and cognitive models for the behavioral sciences, and publish your findings in leading interdisciplinary journals and conference proceedings
- Develop methods and tools: You will develop open-source methods, datasets, and software that make state-of-the-art AI accessible to behavioral researchers
- Pursue your own agenda: You will have substantial freedom and resources to advance your own research program and establish yourself as an independent scientist
- Teach in an international program: You will plan and teach courses in English at the intersection of AI, data science, and behavioral science, and co-supervise bachelor's and master's theses
- Collaborate internationally: You will work within an active international network spanning cognitive science, machine learning, and decision science
- Grow your scientific profile: You will use the postdoc phase to expand your networks, gain experience in grant acquisition, and prepare the next step of your academic career
What you have to offer
- Academic degree: You have completed (or are about to complete) a doctoral degree in psychology, cognitive science, computer science, or a related field with excellent results
- Research focus: Your research addresses large language models in the context of the behavioral sciences – for example, as methodological tools, as measurement instruments, or as models of human cognition
- Publication record: You have published in international peer-reviewed journals or conference proceedings relevant to the field (e.g., behavioral research methods, computational linguistics)
- Technical expertise: You have strong programming skills in Python, hands-on experience with open-source language models and embedding methods (e.g., Hugging Face ecosystem), and a commitment to open and reproducible science; experience with R is an asset
- Conceptual depth: You are interested in the theoretical and conceptual foundations of machine cognition and can connect empirical work to broader questions in cognitive science
- Teaching readiness: You have experience creating accessible scientific materials (e.g., tutorials, workshops) and are prepared to use multimedia teaching and learning formats
- Language skills: You have excellent English skills; German skills are not mandatory but an advantage
- Engagement: You work independently, take initiative, and enjoy collaborative work in an interdisciplinary team
What we offer you
- Six weeks of vacation/Extra vacation time
- Flexible working hours
- Training opportunities
- A wide range of benefits, from an in-house medical officer to athletic activities and a meal allowance to a variety of employee discounts
- Meaningful work in an open-minded, inclusive, and family-friendly environment
- A modern campus with spectacular architecture in the heart of Vienna
- Inspiring campus life with over 2,400 employees in research, teaching, and administration and approximately 21,500 students
- Excellent accessibility by public transportation
The minimum monthly gross salary amounts to €5,014.30 (14 times per year). This salary may be adjusted based on equivalent prior work experience. In addition, we offer a wide range of attractive social benefits.
Do you want to join the WU team?
Then please submit your application by July 22, 2026 (ID 2849).
We are looking forward to hearing from you!
WU is committed to diversity and inclusion and actively encourages applications from people of all backgrounds. We particularly welcome applications from women. Not sure if your profile fully matches our requirements? Apply anyway! In case of equal qualification, female candidates will be given preference. Applicants with disabilities will be supported throughout each stage of the recruitment process. Candidates' qualifications will be assessed in the context of their academic age.