Master Thesis in Causal Machine Learning
Master Thesis in Causal Machine Learning
Robert-Bosch-Campus 1, 71272 Renningen, Germany
Full-time
Robert Bosch GmbH
Company Description
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.
The Robert Bosch GmbH is looking forward to your application!
Job Description
Causal inference is one of the major challenges in AI and a core task in many disciplines. The use of synthetic but realistic data is a promising approach as it allows arbitrary parameter settings and the generation of interventional data.
- During your thesis, you will benchmark the existing causal inference methods.
- You will develop new causal inference methods and application-driven evaluation metrics.
- Finally, you will work as part of a global research team in the area of Causal Discovery and Inference, serving different use cases within Bosch.
Ideally, the results of your work will be part of a scientific publication.
Qualifications
- Education : Master studies in Computer Science, Mathematics or comparable
- Experience and Knowledge : strong programming skills in Python, solid mathematical skills, knowledge in Graphical Models is preferable
- Personality and Working Practice : you like to contribute your ideas to the team and you find it easy to communicate with many different stakeholders
- 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)
49 152 34604222
Jürgen Lüttin (Functional Department)
49 711 811 20059
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