Nils M. Kriege
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PhD Position in Graph Learning – Machine Learning with Graphs, University of Vienna University of Vienna in Austria
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Mar 26, 2026
Country
Austria
University
Universität Wien

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Where to contact
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About this position
The University of Vienna invites applications for a PhD position in Graph Learning within the Machine Learning with Graphs research group, led by Professor Nils M. Kriege at the Faculty of Computer Science. This opportunity is ideal for candidates passionate about advancing the boundaries of machine learning, graph theory, and algorithmics. The group focuses on developing innovative methods and learning algorithms for structured data, with applications spanning chemoinformatics, bioinformatics, computer vision, and social network analysis. The research aims to exploit the potential of graph and network data to automate, accelerate, and improve decision-making processes across diverse domains.
As a University assistant (predoctoral), you will actively participate in research projects, contribute to scientific publications, and present your findings at international conferences. You are expected to finalize your dissertation agreement within 12 months and work towards completing your doctoral thesis. The role also includes independent teaching responsibilities and administrative tasks in line with the university's collective bargaining agreement.
Applicants must hold a completed Master's degree or Diploma in computer science or a related field. Candidates nearing completion of their studies are welcome to apply, but hiring is contingent upon degree completion. Essential qualifications include a solid background or strong interest in machine learning, graph theory, and their mathematical foundations, as well as solid programming skills and familiarity with machine learning libraries (or willingness to learn). Excellent English proficiency and teamwork skills are required. Additional desirable qualifications include experience in research methods, academic writing, and teaching.
The University of Vienna offers a supportive and inspiring international academic environment, flexible working hours with partial remote work, excellent public transport connections, and access to over 600 free internal training courses. The base salary is EUR 3,776.10 (full-time basis; 14 payments per year), with increases for credited professional experience. The initial employment duration is 1.5 years, automatically extended to 3 years unless terminated within the first 12 months, and may be extended up to 4 years with satisfactory progress.
Application documents include an academic CV, letter of motivation (with ideas for a prospective doctoral project), abstract of your master's thesis, degree/diploma certificates, transcript of records, and a list of publications or evidence of teaching experience if available. The University of Vienna is committed to equal opportunities, diversity, and the advancement of women, and encourages qualified female candidates to apply. The application deadline is March 26, 2026. For further information or questions, contact Prof. Nils M. Kriege at [email protected].
Apply online via the provided application link to join a vibrant research community and contribute to cutting-edge developments in graph learning and machine learning.
Funding details
Available
What's required
Applicants must have a completed Master's degree or Diploma in computer science or a related field. Candidates close to completing their studies may apply, but hiring requires completion of the degree. Solid background or strong interest in machine learning, graph theory, and their mathematical foundations is required. Solid programming skills and experience with machine learning libraries (or willingness to acquire) are expected. Excellent command of English and ability to work in a team are necessary. Desirable qualifications include basic experience in research methods, academic writing, and teaching experience.
How to apply
Prepare your academic curriculum vitae, letter of motivation with ideas for a prospective doctoral project, abstract of your master's thesis, degree/diploma certificates, transcript of records, and list of publications or teaching experience if available. Submit your application via the provided application link before the deadline. For questions, contact Prof. Nils M. Kriege at the listed email address.
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