At the Data and AI cluster
at the Eindhoven University of Technology, we aim at developing foundations of AI for the present and the future. This includes the design of new AI methods, development of AI algorithms and tools with a view at expanding the reach of AI and its generalization abilities. In particular, we study foundational issues of robustness, safety, trust, reliability, tractability, scalability, interpretability and explainability of AI. We seek for several postdoctoral researchers (1-3 years) to enhance our strengths in research and education.
The successful candidate will work with topics related to theoretical, application-inspired and/or applied AI and machine learning, and will play a significant role in one or more of the current and future research projects in the group, including but not limited to:
- NWO Perspectief Project Personalized Oncology, involving decision support systems for better personalized care.
- H2020 SmartChange, where our part includes self-audit of (federated) ML models on accuracy, robustness, and fairness, and actionable counterfactual explanations that can be translated into coaching recommendations.
- EDF KOIOS, where our part is to study different intersections of robustness (including adversarial), explainability and frugality, e.g. lack of compute resources on an edge device or relevant data in continual learning).
- H2020 ENFIELD, in which we study next-generation brain-inspired continual AI algorithms that are more robust to distribution shifts and adversarial attacks and that can continuously adapt to the environment.
- MATTER, where we study how to make use of Knowledge Graphs and Large Language Models for helping service engineers to satisfy their information needs.
Broad research topics of interest include, but are not limited to:
- Trustworthy AI.
- Robustness in ML.
- Frugal AI and Green AI on evolving data.
- Interpretable and explainable AI.
- Decision making under uncertainty.
- Probabilistic ML, Probabilistic graphical models, Tractable probabilistic models.
- Knowledge Graphs.
- Large Language Models.
- Self-audit and performance profiling of ML models.
- Use cases of AI/ML in personalized healthcare, behavior change, defense, pig/poultry industry.
There will be opportunities for collaboration with multiple researchers inside and outside the university. The successful candidate will also contribute to educational tasks of the group, including the involvement in advanced courses in AI and machine learning, as well as (co-)supervision of students in all levels.