PhD Opportunity in Robust & Certifiable AI
I am looking for PhD candidates interested in the foundations of machine learning evaluation under distribution shift, with direct links to validation and certification procedures in safety-critical applications (e.g., in collaboration with TÜV Austria). The focus is on mathematically grounded methods (learning theory, estimation, guarantees, uncertainty quantification), rather than purely empirical modeling.
Research directions include:
risk estimation under distribution shifts (e.g., learning representations for two-sample tests)
automated validation procedures (e.g., synthetic data generation)
You are a good fit if you are interested in developing work at the level of the following papers, which define the expected level and style of a PhD in this direction:
M.-C. Dinu et al., “Addressing parameter choice issues in unsupervised domain adaptation by aggregation,” ICLR (oral, <2% of submissions), 2023
P. Setinek et al., “SIMSHIFT: A benchmark for adapting neural surrogates to distribution shifts,” preprint, 2025
W. Zellinger, “Binary losses for density ratio estimation,” ICLR, 2025
K. Schweighofer et al., “Safe and certifiable AI systems: Concepts, challenges, and lessons learned,” TÜV AUSTRIA Report, 2025
Positions are typically embedded in larger institute activities and third-party projects. If this aligns with your interests, please reach out with a short note explaining how your background connects to these topics.