Master Thesis in Causal Machine Learning
Master Thesis in Causal Machine Learning
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Job Description
Causal reasoning is one of the main challenges in AI and a core task in many scientific and engineering disciplines. Accurate causal models enable robust behavior in Out-of-Distribution scenarios which is essential for reliable inferences and Root-Cause-Analysis in real-world applications. However traditional causal models are often computationally intractable limiting their scalability to high-dimensional data and complex scenarios. To address these limitations this master thesis will explore the combination of Large Language Model (LLM) agents with data-driven causal reasoning. The goal is to develop scalable and mathematically sound methods for Causal Machine Learning.
- During your thesis you will study and implement new scalable methods within Causal Machine Learning.
- You will collaborate with a global research team specialized in Causal Discovery Causal Inference and Root-Cause-Analysis.
- Ideally your contribution will be part of a scientific publication and will have a real impact on Bosch use-cases.
Qualifications :
- Education: Master studies in the field of Computer Science Mathematics Data Science Statistics Physics or comparable
- Experience and Knowledge: strong programming skills in Python; solid mathematical skills; prior knowledge in Graphical Models is preferable
- Personality and Working Practice: you excel at staying motivated in your tasks communicating effectively with team members and collaborating as a team player
- Languages: very good in English
Additional Information :
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Nicholas Tagliapietra (Functional Department)
Jrgen Lttin (Functional Department)
49 9
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
