Master Thesis in Causal Machine Learning
Master Thesis in Causal Machine Learning
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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 applicationdriven 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)
Jrgen Lttin (Functional Department)
49 9
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
