PhD Position in Responsible AI
Join a newly established research group at the University of Vienna to advance safe, transparent, and data-centric machine learning.
Best for:Best for highly motivated researchers with a strong background in machine learning, mathematics, and Python programming.
Overview
The Responsible Machine Learning Group at the University of Vienna, led by Prof. Dr. Martin Pawelczyk, is seeking three PhD researchers to join its founding cohort. The group focuses on the intersection of AI safety and data-centric AI, aiming to develop more reliable and human-aligned large-scale models. Successful candidates will contribute to research in areas such as machine unlearning, mechanistic interpretability, and multi-agent systems. This position offers the opportunity to shape the research culture of a fast-growing team while pursuing a doctoral degree in a world-class academic environment.
What you'll do
- Develop and evaluate novel machine learning methods with a focus on data-centric AI, agentic systems, and explainable AI.
- Pursue and complete a PhD thesis within the four-year appointment period.
- Publish research findings in top-tier international conferences and journals.
- Join the Vienna Doctoral School of Computer Science and formalize a doctoral thesis agreement within the first year.
- Contribute to the academic community by assisting in the organization of international meetings and symposia.
- Support the research group through administrative tasks and participate in teaching and student supervision.
Requirements
Degree required: Master’s degree in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related quantitative field.
Degree offered: PhD
Language: English (excellent proficiency required).
Instruction: English
Publications: Early track record of research (e.g., Master's thesis, open-source contributions, or workshop papers).
- Solid background in machine learning, statistics, and mathematics.
- Strong programming skills in Python.
- Proficiency in at least one major deep learning framework such as PyTorch or JAX.
- Early research track record demonstrated through a high-quality Master's thesis, open-source contributions, or workshop papers.
- Excellent command of written and spoken English.
- Must hold or be near completion of a Master's degree in a relevant quantitative field.
Funding & terms
How to apply
Deadline: 2026-10-28 (UTC)
Interested candidates should submit an academic CV, a cover letter detailing their motivation and earliest start date, a copy of their thesis or an extended abstract, and official transcripts. Applicants are also encouraged to complete the provided online form.
- Academic CV
- Cover letter
- Master's thesis or extended abstract
- Bachelor's degree transcripts
- Master's degree transcripts
Contact
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