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# Research position in Artificial Intelligence

- **Company:** Ludwig-Maximilians-Universität München
- **Location:** München, Germany
- **Contract:** Internship / Graduate position
- **Skills:** Artificial Intelligence, Machine Learning, Reinforcement Learning, Information Technology, AISTATS, Machine Learning Operations
- **Published:** October 29, 2026
- **Apply:** https://www.careerjet.de/jobad/de7bf188df6c7dd05a13a2486686b56343

## About the Role

The candidate has a Master's degree in one of the following subjects:

 * Mathematics
 * Statistics
 * Computer Science
 * Data Science
 * Machine Learning

Our team language is English, so fluency in English is required.

## Description

in Munich
Your tasks and responsibilities:
We are seeking highly motivated candidates with a passion for research and a drive to make groundbreaking contributions in the field of AI. Research outlook is a top-priority for the position. Candidates will also gain experience in teaching at the undergraduate and/or graduate level and may supervise student theses.
Generally, PhD students can pursue either (A) a method track or (B) an application track (or a combination thereof).
In the (A) method track, the aim is to make contributions to the top-tier outlets from ML/AI (e.g., NeurIPS, ICML, AISTATS, KDD). Here, we typically focus broadly on methods around causal machine learning. This includes (but is not limited to):

 * causal machine learning
 * causal representation learning,
 * reinforcement learning,
 * off-policy evaluation/learning,
 * probabilistic ML / uncertainty quantification (e.g., diffusion models, Bayes)
 * etc.

In the (B) application track, we aim to advance decision-making through new AI/ML tools across various application areas (e.g., sustainability, medicine, marketing). Here, PhD students regularly publish in various high-impact outlets (e.g., Nature Communications, Nature Medicine, Management Science, KDD) If desired, PhD students can collaborate actively with partners (e.g., Harvard Medical School, Cambridge Centre for AI in Medicine) to solve the big problems of time. The focus is to show the value created by new AI/ML tools and to apply them in practice. This includes:

 * AI/ML for better decision-making in medicine
 * AI for Good to promote sustainable development
 * AI/ML for business decision-making
 * Human-AI collaboration

Our research group is always open to own research proposals and further suggestions.