Do you want to participate in Austria's largest joint project on the fundamentals of Artificial Intelligence and contribute to WU Vienna’s vision of boosting AI? Do you want to understand how things are connected and make a fundamental impact? We are seeking highly motivated and talented individuals to join the Institute for Retailing and Data Science (Prof. Nils Wlömert) and the Institute for Statistics and Mathematics (Prof. Kurt Hornik) at one of Europe’s largest and most modern business and economics universities on a campus where quality of work is also quality of life. We are looking for support at the
Institute for Retailing & Data Science and the Institute for Statistics and Mathematics
Part-time, 30 hours/week
Starting June 01, 2025, and ending after 4 years
The successful candidate will conduct cutting-edge research on combining symbolic and sub-symbolic AI in collaboration with our partners at Johannes Kepler University Linz, AAU Klagenfurt, ISTA, TU Graz, and TU Vienna.
The vision of Bilateral AI is to educate a new generation of top-quality AI scientists with a holistic view of symbolic and sub-symbolic AI methods. Training and mentoring young researchers is a central activity combining groundbreaking research work with an education program. The training will be distributed over the six participating institutions.
We are currently inviting applications for a Project Staff Member position (PhD position). The successful candidate will work under the main supervision of Prof. Nils Wlömert (Institute for Retailing and Data Science) and Prof. Kurt Hornik (Institute for Statistics and Mathematics). The Institute for Retailing and Data Science applies AI and data-driven techniques to solve real-world business problems, particularly in retailing and consumer behavior. The Institute for Statistics and Mathematics focuses on advancing methods in statistics, data science, and AI, particularly in areas that emphasize rigorous, quantitative approaches. Together, these institutes foster a collaborative and interdisciplinary environment with access to strong research infrastructure and unique data, enabling innovative research at the intersection of theory and application in Artificial Intelligence. Moreover, the PhD is expected to collaborate on a broader level under the co-supervision of another key researcher from the BILAI network (https://www.bilateral-ai.net/consortium/).
What to expect
Research area:
The research for this position focuses on integrating symbolic and sub-symbolic AI methods to enhance interpretability and decision-making in AI systems. We primarily aim to contribute to research modules 5 (Causality) and 6 (Explainability) (https://www.bilateral-ai.net/research/research-modules). Potential topics include:
(i) Combining dense vector representations with knowledge graphs for applications such as recommender systems, retail management, and fraud detection, enhancing transparency through structured, symbolic relationships between entities.
(ii) Applying causal inference with contextual embeddings to assess the impact of marketing actions, providing rule-based, interpretable insights into customer behavior and strategy effectiveness.
(iii) Creating explainable AI methods to make AI-driven decisions, such as those of recommender systems and targeting algorithms, more transparent and understandable for consumers.
(iv) Developing novel methods for causal inference with text-based treatments,
leveraging natural language processing and causal representation learning
techniques. Applications will focus on impact evaluation in recommender systems
and retail management, providing insights into how textual content influences
consumer behavior.
Candidates will be trained within the Bilateral AI Doctoral School (https://www.bilateral-ai.net). Joint seminars, scientific workshops, and compulsory courses outside the PhD students’ research fields will also be designed to encourage interdisciplinarity. Apart from that, students will be involved in grant applications, conference organization, Bachelor and Master student supervisions, and teaching. Each student will be supervised by two experienced and internationally renowned professors with different research fields (symbolic / sub-symbolic AI). The training will also provide a career development program, advice and support for students with innovative business ideas, and workshops for presentation and soft skills. We offer a stimulating environment that fosters both academic excellence and professional development, with ample resources and support to pursue innovative research ideas.
