University assistant predoctoral

The University of Vienna is a community of over 10,000 individuals, including approximately 7,500 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 - researchgroup DM & ML

39 Faculty of Computer Science

Job vacancy starting: 02/01/2026 | Working hours: 30,00 | Classification CBA: §48 VwGr. B1 Grundstufe (praedoc)

Limited contract until: 01/31/2030

Job ID: 4938

Explore and teach at the University of Vienna, where more than 7,500 academics thrive on curiosity in continuous exploration and help us better understand our world. Does this sound like you? Then join our accomplished team

Your personal sphere of influence:

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. Tschiatschek, develops methods of machine learning and artificial intelligence, especially in the areas of reinforcement learning and deep probabilistic models.

To strengthen our team, we are seeking a university assistant to develop advanced machine learning methods for improving the simulation and optimization of distributed systems, for instance by specializing neural ODEs and their training routines.

This research will address challenges driven by the energy transition, which is transforming electrical power systems (EPS) into low-inertia, highly digitalized, power-electronics-driven architectures with increasingly interdependent component and system dynamics. Traditional simulation and optimization approaches, which separate EPS analysis and simulation by timescale into isolated zones with independent models and solvers, are no longer sufficient.

New methods are needed to tackle emerging challenges across timescales, from real-time operational control to long-term planning for optimization and expansion. This creates a demand for fast, flexible modeling frameworks, such as surrogate models to accelerate high-fidelity simulations, physics-informed neural networks, neural ODEs, and other hybrid physics–ML approaches.

The contract term for employment is 4 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 4 years if the employer does not issue a non-renewal notice within the first 12 months.

Your future tasks:

You will be actively involved in research, teaching and administration, which means

• Participation in research projects / scientific studies Participation in publications / scientific articles / lecturing activities
• Participation in conferences and publication of your research results in journals and at conferences
• Conclusion of a dissertation agreement within 12 months (if not already in place)
• Participation in and independent teaching of courses in accordance with the provisions of the collective agreement
• Supervision of students
• Participation in the organization of meetings, conferences, symposia
• Participation in the administration of the institute, teaching and research

Requirements:

Completed Master's degree (or comparable degree) in computer science, mathematics, electrical engineering, information processing or a related fiel

• d
  High analytical skill- s
  Experience in the field of interdisciplinary researc- h
  High level of written and oral communication skill- s
  Very good command of written and spoken Englis- h
  IT user skill- s
  Cooperative, team-oriented and proactive behavio- r
  Excellent knowledge in the areas of human-computer interaction, explainable AI, qualitative research, planning and conducting interview studie- s
  Excellent academic academic achievements, especially first publications in the field of explainable AI with a focus on understanding algorithms by various stakeholders, especially at ACM FAccT, ACM AIES, International Journal of Human-Computer Studies or simila- r
  Strong written and oral communication skill- s

The following are also desirable:

Teaching experienc

• e
  Knowledge of university processes and structure- s
  Experience abroa- d

Your profile:

Master's degree (or comparable degree) in Computer Science, Data Science, Physics, Computational Science, or electrical engineerin

• g
  Excellent academic achievements, with initial research results in the relevant field, documented through publications or manuscripts in preparatio- n
  Excellent knowledge in the area of scientific machine learning, including topics like Physics-Informed Neural Networks (PINNs), Neural ODEs, Bayesian learning, and system modeling and simulatio- n
  Strong background in numerical methods and machine learnin- g
  Good programming skills, preferably in Pytho- n
  Experience with deep learning frameworks such as PyTorch or TensorFlo- w
  Excellent command of English and German (spoken and written- )
  Strong ability to work collaboratively in research team- s
  Perseverance and ability to complete projects reliabl- y
  High motivation and commitment to scientific excellenc- e
  Willingness to travel, including attendance at national and international conference- s

Desirable additional qualifications:

Research experience applying physics-informed machine learning or comparable methods that integrate physical laws into data-driven model

• s
  Teaching experienc- e
  Knowledge of university processes and structure- s

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 basic salary of EUR 3714,80 (on a full-time basis) increases if we can credit professional experience.

Equal opportunities for all: We welcome every additional/new personality to the team

It is that easy to apply:

Cover letter / letter of motivation; in particular, briefly describe your suitability and previous knowledge with regard to the call (max. 2 pages

• )
  Academic curriculum vitae / letter of intent (including a description of your teaching experience, if available- )
  List of publication- s
  Proof of teaching experience (if available- )
  Degree certificate- s
  Application via our job portal / Apply now butto- n

If you have any questions, please contact:

Sebastian Tschiatschek

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.

University of Vienna. Space for personalities. Since 1365.

Data protection

Application deadline: 12/08/2025

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