Master Thesis in Causal Machine Learning

Robert Bosch Group; Renningen; Vor Ort; EUR 60.000 - 80.000; Vollzeit

Zusammenfassung

The Robert Bosch GmbH is offering a Master Thesis in Causal Machine Learning that explores combining Large Language Model agents with causal reasoning. The selected candidate will implement scalable methods, collaborate with a global research team, and ideally contribute to scientific publications, enriching their academic experience.

Qualifikationen

• Strong programming skills in Python.
• Solid mathematical skills.
• Prior knowledge in Graphical Models is preferable.
• Very good in English.

Aufgaben

• Study and implement new scalable methods within Causal Machine Learning.
• Collaborate with a global research team specialized in Causal Discovery.
• Contribute to scientific publications.

Kenntnisse

Python

Mathematics

Causal Inference

Graphical Models

Ausbildung

Master studies in Computer Science, Mathematics, Data Science, Statistics, or Physics

Jobbeschreibung

Master Thesis in Causal Machine Learning

• Full-time

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

TheRobert Bosch GmbHis looking forward to your application!

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.
• 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

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)
[phone]
Jürgen Lüttin (Functional Department)
[phone]