PhD Positions in Responsible AI Universität Wien

Job description:

At the University of Vienna, almost 11,000 people work together on the big questions of the future. Approximately 7,700 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 offering

PhD Positions in Responsible AI

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.

About the team

Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research sits at the intersection of AI Safety and Data-Centric AI. We aim to make large-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in:

• Data-centric AI: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. 

• AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems 

• Efficiency:  Streamlining large-scale model experimentation and training. 

• Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. 

As an early member of this fast-growing team, you will have a unique opportunity to shape our research culture and contribute to high-impact projects from the ground up. We are currently hiring a founding cohort of 3 PhD researchers to tackle high-impact projects across our core focus areas.  We are looking for curious minds who are excited to push the boundaries of responsible AI. 

Learn more about the lab's work at: https://martinpawelczyk.github.io/. 

Tasks and Responsibilities

• Develop machine learning methods and tools with a specific focus on:
  • Data-Centric AI: Including data attribution, data curation, and privacy preservation for large foundation models (e.g., LLMs and VLMs);
  • Agentic AI: Exploring multi-agent systems and their dynamics;
  • Explainable AI: With a particular emphasis on mechanistic interpretability.
• Invent, evaluate, and publish novel algorithms, aiming for theoretical guarantees when working with structured and unstructured data.
• Pursue and complete a PhD thesis within the appointed 4-year duration.
• Join the Vienna Doctoral School of Computer Science and conclude a doctoral thesis agreement within 12 months.
• Present your research results at top-tier international conferences, workshops, and journals.
• Contribute to the academic community by assisting in the organization of international meetings, conferences, and symposia.
• Take on administrative responsibilities to support the research group and the Faculty of Computer Science. You hold courses and examinations independently and (co-)supervise students within the scope of the provisions of the collective bargaining agreement. 

Your qualifications

Must-haves

• A Master’s degree (completed or near completion) in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related quantitative field. 
• A solid background in machine learning, statistics and/or mathematics.
• Strong programming skills in Python. Ideally, also proficiency in at least one major deep learning framework (e.g., PyTorch, JAX).
• An early track record of research (e.g., a high-quality Master's thesis, open-source contributions, or workshop papers). 
• Excellent command of written and spoken English, as we operate in a global research environment.
• A high degree of intrinsic motivation, curiosity, and the ability to work both independently and collaboratively.

Desirable skills

• Experience in training or fine-tuning large-scale models (LLMs, VLMs) in distributed settings. 
• Familiarity with cluster computing environments (e.g., SLURM) and Linux-based workflows.
• Prior experience in a research lab, including contributions to publications or significant open-source projects.
• Teaching experience.

What we offer

• Competetive Compensation: A full-time salary of EUR 3,776.10 (on a full-time basis, paid 14 times a year) which may increase if we can credit professional experience.
• World-Class Location: Live and work in Vienna, consistently ranked as the world's most livable city, with our office easily accessible via excellent public transport.
• Inspiring work atmosphere: Become part of an international academic team in a healthy and collaborative working environment.
• Training and Coaching: Opportunity to deepen your skills on an ongoing basis. There are over 600 courses to choose from – free of charge.
• Contract Structure: The planned employment duration is up to 4 years. Following standard university procedures, the initial contract is for 1.5 years. This automatically extends to 3 years – and subsequently up to 4 years based on research progress – following a successful mutual evaluation in the first 12 months. 

What you need to apply

• Academic curriculum vitae
• Cover letter (State the earliest start date and your motivation to apply.)
• A copy of your thesis (or an extended abstract if still in progress)
• Official transcripts for both your Bachelor's and Master's degrees
• Fill out this form (optional but encouraged): https://tinyurl.com/mvamwy7a 

If you have any questions, please contact:

Martin Pawelczyk  

martin.pawelczyk@univie.ac.at

We look forward to welcoming new talent 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.

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​Application deadline: 05/20/2026 

Apply now