Background
Learn representations that are invariant to hospital-specific confounders and improve out-of-distribution prediction of patient outcomes. This project sits within the causal inference and healthcare activities of our group at Max Planck Institute for Intelligent Systems, and builds on several recent publications and ongoing collaborations with industry and other research groups.
Objectives
- Review the state of the art in causality and formulate a precise research question
- Design and implement the methodology described above
- Evaluate results rigorously, including ablations and comparison with baselines
- Document the work in a thesis and, where results allow, a conference paper
What you will do
You will join weekly group meetings, work closely with a PhD student as day-to-day supervisor, and have access to our computing/lab infrastructure. We expect independent work, curiosity and good communication. The exact scope is adapted to a master/phd thesis of about 6 months.
What we offer
- Supervision by Clara Fischer and an experienced team
- Financial support (see funding details)
- An international environment and the possibility to continue with a PhD for strong candidates
How to apply
Apply through ThesisAbroad with your CV, transcript and a short motivation explaining why this topic interests you. Shortlisted candidates are invited to a 30-minute video call.
Requirements
- Enrolled in a master or PhD-track programme in Artificial Intelligence or a related field
- Solid background in causal inference
- English (B2 or higher)
- Programming or lab experience relevant to the topic
- Ability to spend the full duration on site