• 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.
• Applying causal inference with contextual embeddings to assess the impact of marketing actions, providing rule-based, interpretable insights into customer behavior and strategy effectiveness.
• Creating explainable AI methods to align targeting policies with GDPR regulations, making AI-driven decisions more transparent and understandable for consumers.
• Developing brand-specific representations in shared embedding spaces to adapt AI-generated content across modalities, ensuring coherence with brand identity in marketing strategies.

• Machine learning and data science
• Embedding techniques and neural networks
• Quantitative marketing, econometrics, or applied statistics
• Causal inference and explainable AI
• Recommender systems or other relevant areas of marketing analytics
• Knowledge representation and reasoning
• Graph theory, knowledge graphs, or decision support systems
• Multimodal content generation

• Inspiring campus life with over 2,400 employees in research, teaching, and administration and approximately 21,500 students
• A modern campus with spectacular architecture in the heart of Vienna
• Excellent accessibility by public transportation
• Meaningful work in an open-minded, inclusive, and family-friendly environment
• Flexible working hours
• A wide range of benefits, from an in-house medical officer to athletic activities and a meal allowance to a variety of employee discounts