Open PhD positions# and PostDoc positions# in the Cluster of Excellence “Bilateral AI” at WU Wien

Dear colleagues,


We are running a large national Cluster of Excellence, funded by the Austrian Science Funds  on the topic of combined approaches for building more robust AI systems, "Bilateral AI", that leverage and combine on a foundational level Symbolic Methods and SubSymbolic Machine Learning techniques including generative models,  with an emphasis on leveraging also techniques such as Knowledge Graphs and Semantic Web technologies as a main “ingredient”. If you want to join this exciting project, which involves 6 Austrian universities, we are very much looking forward to applications for the positions announced below!


Best regards,

Axel Polleres



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Open PhD positions# and PostDoc positions# in the Cluster of Excellence “Bilateral AI” at WU Wien (Vienna University of Economics and Business)

We are seeking highly motivated and talented individuals to join our dynamic research team for combining symbolic and sub-symbolic AI. The successful candidates will conduct research at the Vienna University of Economics and Business (WU Vienna) in collaboration with our partner institutes Johannes Kepler University Linz, AAU Klagenfurt, ISTA, TU Graz, and TU Vienna.

The vision of Bilateral AI<https://www.bilateral-ai.net/> is to educate a new generation of top-quality AI scientists with a holistic view on symbolic and sub-symbolic AI methods. Training and mentoring of young researchers is a central activity, which combines groundbreaking research work with an education program. The training will be distributed over the six participating institutions.

Position 1 - PhD (pre-doc) position (4 years)

Organizational Unit: Institute for Data, Process, and Knowledge Management

Principal Investigator: Univ.Prof. Dr. Axel Polleres

Research Focus: Graph-based structures are highly relevant to all the essential properties of BILAI’s vision of broad and more robust AI. Graph-based structures are inherently symbolic and often equipped with sub-symbolic attributes such as costs or interaction strength. They are omnipresent when solving complex tasks, and can appear as navigation maps, as social or physical interaction networks, or as object relations. Graphs are ideal to transfer knowledge: their nodes and edges represent learned or known abstractions of real-world entities in so-called Knowledge Graphs (cf. for instance, http://kgbook.org<http://kgbook.org/>); their structure is typically very robust; they can be readily adapted to new situations or even constructed on the fly; they allow for advanced reasoning; and they allow employing efficient algorithms from computer science. Because of the inherent symbolic nature and their suitability for learning and sub-symbolic elements, they naturally constitute a promising starting point as a core component for a bilateral AI approach.

The proposed PhD project aims to advance the state of the art in this field in working towards

  1.  investigating and understanding the development and evolution of graph structures in real-world (Knowledge) Graphs (KGs)

  2.  connecting networks of KGs and other structured data corpora, leveraging ML and hybrid AI approaches (incl., for instance, foundation models and RAG), as well as graph modularization and federation techniques

  3.  leveraging both symbolic constraints and graph embeddings and learning approaches to the field of graph data quality improvements & repairs.



Where to apply: https://wirtschaftsuniversitaet-wien-portal.rexx-systems.com/Project-Staff-Member-PhD-position-eng-j2257.html



Position 2 - PhD (pre-doc) position (4 years)

Organizational Unit: Institute for Complex Networks

Principal Investigator: Assoz.Prof PD Dr. Sabrina Kirrane

Research Focus: Given that machine learning (ML) systems are increasingly being applied to and trained on user-generated data, data protection has become a crucial component of trustworthy and ethical ML systems. However, in this context, adhering to data protection principles is particularly challenging, as ML algorithms are often opaque and could potentially infer confidential information during the training process. The proposed PhD project aims to extend both the symbolic and the sub-symbolic state of the art. The goals are threefold:

  1.  to provide guarantees that ML models trained on KGs adhere to privacy policies;

  2.  to develop new ML algorithms that are privacy preserving; and

  3.  to develop methods with practical relevance as well as provable guarantees, which we will actively promote as tools, towards standard-compliant and more trustworthy AI systems.



Where to apply: https://wirtschaftsuniversitaet-wien-portal.rexx-systems.com/Project-staff-member-PhD-Position-eng-j2254.html



Position 3 - PhD (pre-doc) position (4 years) or an Assistant Professor (post-doc) position (2.5 years)

Organizational Units: Institute for Retailing and Data Science; and Institute for Statistics and Mathematics

Principal Investigators: Univ.-Prof. Dr. Nils Wlömert; and Univ.-Prof. Dr. Kurt Hornik

Research Focus: The research for this position focuses on integrating symbolic and sub-symbolic AI methods to enhance interpretability and decision-making in AI systems. Potential topics include:

  1.  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.

  2.  Applying causal inference with contextual embeddings to assess the impact of marketing actions, providing rule-based, interpretable insights into customer behavior and strategy effectiveness.

  3.  Creating explainable AI methods to align targeting policies with GDPR regulations, making AI-driven decisions more transparent and understandable for consumers.

  4.  Developing brand-specific representations in shared embedding spaces to adapt AI-generated content across modalities, ensuring coherence with brand identity in marketing strategies.



Where to apply: https://wirtschaftsuniversitaet-wien-portal.rexx-systems.com/Project-staff-member-PhD-Position-or-Assistant-Professor-p-eng-j2256.html



Important Information

All appplications need to be submitted online via the WU recruitment system (https://www.wu.ac.at/en/careers/careers-at-wu/current-job-openings). Individual links are provided above for your convenience. Applications to multiple positions are permitted, however please ensure that you apply separately to each position. Only complete applications will be processed. For specific details, in terms of the job description and the documents that need to be submitted, please vist the respective job announcements.

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Prof. Dr. Axel Polleres
Institute for Data, Process and Knowledge Management,
Department of Information Systems and Operations Management, WU Vienna
url: http://www.polleres.net/