"University assistant predoctoral - PhD Position in Graph Learning"

The University of Vienna is a community of almost 11,000 individuals, including approximately 7,700 academic staff members, who passionately pursue answers to the profound questions that shape our future. They represent individuals driven by curiosity and a relentless pursuit of excellence. With us, they find the space to try things out and unfold their potential. Are you inspired by their passion and determination? We are currently seeking a/an

University assistant predoctoral - PhD Position in Graph Learning

39 Faculty of Computer Science
Startdate:01.05.2026 | Working hours:30 | Collective bargaining agreement:§48 VwGr. B1 Grundstufe (praedoc)
Limited until:30.04.2029
Reference no.:5311

Your responsibilities:

As a University assistant, you will contribute to the work group Machine Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims at exploiting the potential of the growing amount of structured data in all these areas to automate, accelerate and improve decision making. Analysing graph data requires solving problems at the boundaries of machine learning, graph theory, and algorithmics.

Your future tasks:

You actively participate in research, teaching & administration, which means:

• You are involved in research projects in the area of graph learning.
• You are working on scientific articles to publish your research results.
• You attend scientific conferences to present your research.
• We expect you to finalize your dissertation agreement within 12 months.
• You work on your dissertation and its completion.
• You contribute to courses independently within the scope of the provisions of the collective bargaining agreement.
• You take on administrative tasks in research, teaching and administration.

Your research should be situated in the field of machine learning with graphs and may address both theoretical and practical questions. The aim is to analyse and develop new, well-founded methods and learning algorithms that extend the boundaries of existing techniques - for example, with respect to expressivity, generalization, interpretability, or scalability.

This is part of your personality:

• Completed Master's degree or Diploma, in the field of computer science or a related field (Applications from candidates who are close to completing their studies are welcome; hiring can only take place once the Master’s/Diploma degree has been completed.)
• Solid background or strong interest in machine learning, graph theory, and their mathematical foundations.
• Solid programming skills
• Experience with machine learning libraries or willingness to acquire
• Excellent command of English
• Ability to work in a team

Desireable qualifications:

• Basic experience in research methods and academic writing
• Teaching experience

What we offer:

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 base salary is EUR 3.776,10 (full-time employement basis; 14 payments per year); it increases if we can credit professional experience.

Tenure: The employment duration is 3 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 3 years if the employer does not terminate it within the first 12 months by submitting a declaration of non-extension. With appropriate work progress, an extension to a total maximum of 4 years is possible.

Application documents:

• Academic curriculum vitae
• Letter of Motivation including ideas for a prospective doctoral project
• Abstract of master´s thesis
• Master´s Degree / Diploma certificates
• Transcript of records
• List of publications, evidence of teaching experience (if available)

If you have any questions, please contact:

Nils Morten Kriege
nils.kriege@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 place particular emphasis on enhancing women’s representation among the academic and general university staff, particularly in leadership roles, and therefore expressly encourage qualified women to apply. Given equal qualifications, preference will be given to female candidates.

University of Vienna. Space for personalities. Since 1365.

Application deadline: 03/26/2026