Open Positions

I am looking for motivated researchers who are eager to learn, contribute, and help advance Causal AI to tackle challenging problems across biomedical and broader life sciences.

The positions involve research at the intersection of causal discovery and inference, Bayesian and probabilistic machine learning methods for uncertainty quantification and integration of expert knowledge, multimodal data modeling, and causal abstraction and representation learning. The goal is to develop causal frameworks that operate robustly and efficiently on large-scale multimodal biomedical datasets, addressing challenges such as data heterogeneity, latent confounding, selection bias, and distributional shifts.

Candidate Profile

A strong background in mathematics, computer science, or a related field is expected. Experience in any of the research topics above is a plus. If you are motivated to grow and contribute to causal and explainable AI for the life sciences, I encourage you to apply.

For guidance: If you would like to better understand the fundamentals of causal AI, you may find it helpful to explore the online lectures and talks available on my YouTube channel.

How to Apply

Please send the following materials in a single email:

1. CV
2. Names and contact information of two references
3. Research statement (up to two pages) describing your background, research interests, previous contributions, motivation for joining the group, and your expectations and goals for the position. Within the statement, highlight up to three of your most significant publications and detail your contributions.

Use the subject line "Application: Causal AI PhD Position [e01]" or "Application: Causal AI Postdoctoral Position [e02]", depending on the role. You are welcome to get in touch informally before submitting a formal application.