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?
To strengthen our team, we are seeking a university assistant to develop advanced machine learning and artificial intelligence methods.
39 Faculty of Computer Science
Job vacancy starting: 10/01/2026 | Working hours: 30,00 | Classification CBA: §48 VwGr. B1 Grundstufe (praedoc)
Limited contract until: 09/30/2030
Job ID: 5376
The employment duration is 4 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 4 years if the employer does not terminate it within the first 12 months by submitting a non-extension declaration.
The working group “Probabilistic and Interactive Machine Learning” within the research group “Data Mining and Machine Learning” at the Faculty of Computer Science, led by Prof. Sebastian Tschiatschek, develops foundational methods in machine learning and artificial intelligence. We focus particularly on the areas of reinforcement learning, interactive learning, and deep probabilistic models.
While modern reinforcement learning (RL) has achieved remarkable success, it remains limited when applied to complex, open-ended, or poorly defined environments. Two of the most critical bottlenecks in contemporary AI are sample inefficiency, often caused by the lack of intelligent, structured exploration, and the "reward engineering" problem, where designing an explicit scalar reward function that captures desired behavior is incredibly difficult or impossible.
Furthermore, as AI systems are deployed in more complex environments, the challenges of AI alignment (ensuring systems behave according to human preferences) and constrained learning (adhering to strict safety, legal, or physical boundaries) become important.
This position is dedicated to addressing these core challenges by advancing the frontiers of Inverse Reinforcement Learning (IRL), exploration, and safe/aligned AI.
You actively participate in research, teaching & administration, which means:
You will contribute to academic research projects in the above-mentioned areas
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You will work on scientific articles and publish your research results- .
You will participate in academic conferences to present your research- .
We expect you to conclude your dissertation agreement within 12 months- .
You will work on your dissertation and bring it to completion- .
You will take on teaching responsibilities as specified by the collective agreement- .
You will support the supervision of students’ projects and theses- .
You will undertake administrative duties in research, teaching, and university administration, and support the organization of workshops, conferences, and symposia- .
Completed Master's degree (or comparable degree) in computer science, data science, mathematics, communication engineering, or a related field (Applications from candidates close to completion are welcome. Employment can only begin once the Master´s degree has been awarded.
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Excellent command of Englis- h
Outstanding written and oral communication skill- s
Strong ability to collaborate within research team- s
Perseverance and a proven ability to bring projects to reliable completio- n
High motivation and commitment to scientific excellenc- e
Willingness to travel, including participation in national and international conference- s
Solid knowledge of machine learning and artificial intelligence (in particular in the areas of bandits and reinforcement learning- )
Strong programming skills, preferably in Python- .
Experience with deep learning frameworks such as Jax, PyTorch, or TensorFlow- .
Excellent analytical skills and strong interest in developing a deep understanding of algorithms and method- s
Cooperative, team-oriented, and proactive working styl- e
The following are also desirable:
Experience with research methods in the field of machine learning and artificial intelligence, as well as scientific writin
- g
Excellent academic record, ideally with initial research results in the area of the position, documented by publications or manuscripts in preparatio- n
Experience in university teachin- g
International experienc- e
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.
Tenure: The employment duration is 4 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 4 years if the employer does not terminate it within the first 12 months by submitting a non-extension declaration.
Academic curriculum vitae
Letter of motivation including your project ideas (and description of your teaching experience, if available- )
Abstract of Master´s Thesi- s
Degree certificate- s
Transcript of record- s
List of publications, evidence of teaching experience (if available- )
Sebastian Tschiatschek
[email protected]
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.
Data protection
Application deadline: 06/16/2026
