TU Wien Informatics

20 Years

Assistant Professor with Tenure Track of Natural Language Processing

  • 2023-10-12
  • Open Position
  • Professorship

We invite applications for this full-time assistant professorship with tenure track until January 11, 2024.

Assistant Professor with Tenure Track of Natural Language Processing
Picture: Ola La Merkel / stock.adobe.com

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TU Wien is Austria`s largest institution of research and higher education in the fields of technology and natural sciences. With over 26,000 students and more than 4000 scientists, research, teaching and learning dedicated to the advancement of science and technology have been conducted here for more than 200 years, guided by the motto “Technology for People”. As a driver of innovation, TU Wien fosters close collaboration with business and industry and contributes to the prosperity of society.

The Faculty of Informatics at TU Wien invites applications for a full time Assistant Professor position with tenure track. The position is directed to Natural Language Processing for Complexity Science and Societal Insight and is affiliated with both the Institute of Information Systems Engineering, Research Unit Data Science, and the Complexity Science Hub Vienna. The estimated starting date is October 2024. The work contract is initially limited to six years. The candidate and TU Wien can agree upon a tenure evaluation, which when positive, opens the possibility to change the position to Associate Professor with an unlimited contract.

TU Wien is among the most successful technical universities in Europe and it is Austria’s largest scientific-technical research and educational institution. The Faculty of Informatics, one of the eight faculties at TU Wien, plays an active role in national and international research and has an excellent reputation. The main areas of research include Computer Engineering, Logic and Computation, Visual Computing & Human-Centered Technology, as well as Information Systems Engineering.


Complexity Science successfully models multiple aspects of society to provide key insights to decision makers. This success arises through understanding data not in isolation but taking the complexity of interdependencies and interconnections into account. Benefits include using insights obtained from the data to improve public services, or to better understand the impacts on society of changes in legislation or of external factors such as natural disasters.

A data modality that is not used to its full potential in Complexity Science is text from multiple potential sources: government (e.g. patents, legislation, court decisions), commercial (e.g. company annual reports), medical (e.g. patient records), scientific (e.g. scientific papers, historical documents), and civil (e.g. social media). Text analysis approaches need to be developed to reliably extract trustworthy information from potentially noisy text as input to models and simulations. As the text data is often analysed for purposes for which it was not originally collected, it is necessary to compensate for potential biases. The candidate is expected to contribute both to methodological work on text analysis approaches as well as to applied work on solutions to real-world problems in an inter-disciplinary setting.

The candidate will work at the interface of Data Science and Complexity Science together with governments and public administrations, and should therefore also pay attention to presenting results in a suitable way for use by decision makers. The Digital Humanism Initiative on ensuring that technology development remains centred on human interests should play a central role in this work.

Candidates should have a background and experience in building prototypes that target a well-defined application area and analyse real-world data. The following list highlights some potential research topics of a successful candidate:

  • Natural Language Processing
  • Computational Linguistics
  • Text Mining
  • Information Extraction
  • Information Retrieval
  • Experiment design and evaluation
  • Network Science
  • Privacy-aware analytics
  • Data fusion

Furthermore, a successful candidate should have an interest in:

  • Human-in-the-loop
  • Explainability and interpretability
  • Algorithm and data biases

The candidate should have experience in working with industry or public administrations, relevant postdoctoral experience, and a compelling research vision. We are particularly interested in candidates working in areas that will complement our existing expertise and lead to fruitful collaborations with other members of TU Wien Informatics, TU Wien in general, and the Complexity Science Hub Vienna.

Besides research, the duties of an Assistant Professor at TU Wien include graduate and undergraduate teaching (in English and German) as well as contributing to usual management and faculty service tasks.


For additional details and to enter the application process, please see the TU Wien Job Platform:

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