University Assitant (praedoc) - Completion Contract

At the University of Vienna, over 10,000 people work together on the big questions of the future. Approximately 7,500 of them are academic staff members. These are individuals who, with their curiosity and their continuous pursuit of excellence, engage in international cutting-edge research and teaching. With us, you will also find space to unfold your potential. We are looking for a/an 

39 Faculty of Computer Science  

Job vacancy starting: 01.05.2026 | Working hours:  30.00  | Classification CBA: §48 VwGr. B1 Grundstufe (praedoc) 

Limited contract until: 31.08.2026

Job ID: 5544

Among the many good reasons to want to research and teach at the University of Vienna, there is one in particular, which has convinced around 7,500 academic staff members so far. They see themselves as personalities who need space for their continuous striving and curiosity in order to be able to be More than 7,500 academics at the University of Vienna thrive on continuous exploration and curiosity and help us better understand our world. Does this sound like you? Then join our accomplished team!
 

The Doctoral School Computer Science DoCS (https://docs.univie.ac.at ) founded in 2020 offers a structured doctoral programme that supports early-career research in the areas of Computer Science and Business Informatics. The programme includes world-leading researchers in Computer Science as supervisors and co-supervisors who pursue basic as well as applied research within an intensive doctoral teaching programme tailored to the scientific needs of students in small topical groups. 

The Doctoral School Computer Science provides the option of performance-based contract extensions to doctoral students who previously had a position as university assistant (prae doc) or as third-party funded researcher (prae doc) at the University of Vienna. This completion contract is meant to support a high-qualified DoCS fellow in the final phase of the doctoral studies. The successful applicant will get a contract as university assistant (prae doc) and will be working in the research group Data Mining and Machine Learning at the Faculty of Computer Science.

The group’s research focuses on data mining - particularly clustering and representation learning - graph mining, and probabilistic machine learning. We place great emphasis on a relaxed, collaborative environment and a collegial atmosphere. Social interaction is a top priority for us and contributes to active scientific dialogue and a positive work environment.

Learn more about us at https://dm.cs.univie.ac.at/

You will actively participate in research, teaching, and administration. This includes, among other things:

• Your primary responsibility is to complete and submit your dissertation.
• You will develop new methods in data mining and machine learning, particularly in the areas of
  • clustering
  • representation learning
  • graph mining
  • anomaly detection
• You will contribute to research projects and scientific studies in the above-mentioned areas.
• You will independently teach courses in accordance with the provisions of the collective bargaining agreement or assist in doing so.
• You will publish internationally and give presentations.
• You will contribute to the organization of meetings, conferences, and symposia.
• You will participate in faculty, teaching, and research administration. 

Required Qualifications:

• You hold a degree in Computer Science or Computer Engineering (Diplom, Master’s, or Magister degree), and a solid foundation in computer science is required.
• You are currently enrolled in a doctoral program (Computer Science or Business Informatics) at the University of Vienna and are a member of the Doctoral School of Computer Science.
• You have demonstrated outstanding academic performance (e.g., FÖP within one year, annual submission of progress reports, exceptional publication record).
• You possess a high degree of motivation and commitment to successfully complete your doctoral studies within the intended timeframe.
• You have proven expertise in one or more of the following areas:
  • Clustering
  • Representation Learning
  • Graph Mining
  • Anomaly Detection
• You possess teaching skills and experience with e-learning.
• You have excellent written and spoken English skills (at least C1 level). (The language of communication within the team is English!)

Desirable qualifications:

• You have publications in leading journals in the field, e.g., ICCV, KDD, ICDM, WWW
• You are a team player with strong social and communication skills.
• You work independently and reliably.
• You have knowledge of university processes and structures.
• You have international experience
• You have teaching experience

Work-life balance: Our employees enjoy flexible working hours and can partially work remotely. 

Inspiring working atmosphere: You are a part of an international academic team in a healthy and fair working environment.

Good public transport connections: Your workplace is easily accessible by public transport.

Internal further training & Coaching: Opportunity to deepen your skills on an ongoing basis. There are over 600 courses to choose from – free of charge.

Fair salary: The basic salary of EUR 3.776,10 (on a full-time basis) increases if we can credit professional experience.

Equal opportunities for everyone: We look forward to diverse personalities in the team!

• Resume
• Cover letter outlining your ideas for a potential doctoral project
• Abstract of your master’s thesis
• Degree certificates and transcripts with grades from your bachelor’s and master’s programs
• List of publications, proof of teaching experience (if applicable)
  

Claudia Plant  

claudia.plant@univie.ac.at

We look forward to new personalities in our team! 
The University of Vienna has an anti-discriminatory employment policy and attaches great importance to equal opportunities, the advancement of women and diversity . We lay special emphasis on increasing the number of women in senior and in academic positions among the academic and general university staff and therefore expressly encourage qualified women to apply. Given equal qualifications, preference will be given to female candidates.

Data protection
Application deadline: 25.04.2026