Join Us
We are always looking for motivated and talented people to join our group! We value diversity and inclusion, and we strongly encourage applications from underrepresented groups.
Open Positions
PhD Positions
We currently have two open PhD positions in the following areas:
Causal Fairness in Sequential Decision-Making
Work on developing causal notions of fairness for reinforcement learning and sequential decision-making systems. This position is funded through the DFG Priority Programme.Federated Learning with Formal Privacy Guarantees
Develop new federated learning algorithms with rigorous differential privacy guarantees, with applications in healthcare and finance.
Requirements:
- Master’s degree (or equivalent) in Computer Science, Mathematics, Statistics, or a related field
- Strong background in machine learning, probability theory, and statistics
- Programming skills in Python (PyTorch/JAX experience is a plus)
- Good academic writing skills in English
To apply, please send an email to niki.kilbertus@tum.de with the subject line “PhD Application”, including:
- Your CV
- A brief statement of research interests (1 page)
- Transcripts of your academic records
- Names and contact details of two references
Postdoctoral Positions
We occasionally have postdoctoral openings. If you are interested in joining the group as a postdoc, please reach out directly with your CV and a short description of your research interests. Strong candidates may also be supported in applying for independent fellowships (e.g., Marie Skłodowska-Curie, Humboldt Fellowship).
Master’s Thesis
We regularly offer Master’s thesis projects for TUM students in the following areas:
- Causal inference and causal discovery
- Fairness in machine learning
- Privacy-preserving machine learning
- Uncertainty quantification
Please send an email to niki.kilbertus@tum.de with the subject “Master’s Thesis Inquiry”, including your CV, transcript, and a brief description of which research area interests you most.
Internships and Visiting Researchers
We occasionally host research interns and visiting researchers. Please get in touch if you are interested in spending time with the group.
Research Projects
Our group works at the intersection of machine learning, causal inference, and statistics, with a focus on developing methods that are fair, transparent, and privacy-preserving.
Active Projects
Causal Fairness in Machine Learning
We develop causal frameworks to rigorously define and enforce fairness in automated decision-making systems. This project investigates how causal assumptions can help distinguish between legitimate and discriminatory uses of sensitive attributes in predictive models.
Team: Prof. Dr. Niki Kilbertus, Alice Mueller
Funding: DFG Priority Programme SPP 1798
Privacy-Preserving Federated Learning
This project designs federated learning algorithms that enable collaborative model training across distributed data sources without sharing raw data. We provide formal differential privacy guarantees and study the privacy-utility trade-off in real-world applications.
Uncertainty Quantification with Bayesian Causal Models
We combine Bayesian inference with causal graphical models to better quantify and communicate uncertainty in causal effect estimates, with applications in clinical decision support.
Team: Prof. Dr. Niki Kilbertus, Dr. Jane Smith, Clara Vogel
Funding: Helmholtz AI
Past Projects
Causal Discovery from Observational Data
Developed scalable algorithms for learning causal structure from large observational datasets, with theoretical guarantees under various faithfulness and Markov assumptions.
Team: Dr. Eva Fischer
What We Offer
- Exciting research at the frontier of trustworthy machine learning
- A collaborative and inclusive environment
- Regular group meetings, reading groups, and seminars
- Funding for conference travel
- Access to computing infrastructure (GPU clusters)
- Close collaboration with TUM’s broader AI ecosystem
- Location in Munich, one of Europe’s most vibrant cities
Life in Munich
Munich offers an exceptional quality of life, combining a strong tech and research ecosystem with proximity to the Alps, excellent public transportation, and a lively cultural scene. The cost of living is high by German standards, but PhD and postdoc salaries at TUM are competitive.