Thesis in Transfer Learning for Data-Based Control of Hydraulic Cylinders
Thesis in Transfer Learning for Data-Based Control of Hydraulic Cylinders
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Job Description
At Bosch Corporate Research we focus on developing learning control systems for hydraulic cylinders to enhance advanced excavator assistance functions.
- During your thesis you will explore methods for transferring knowledge data and model parameters between similar learning tasks to increase sample efficiency for new tasks.
- You will investigate measures to evaluate the performance of fine-tuned controllers to ensure they meet desired operational standards.
- Furthermore you will develop strategies to reduce the computational load of model fine-tuning to enable real-time model adaptation during regular operation.
- Finally you will create methods for online validation of model adaptations to ensure reliability and performance in dynamic environments.
Qualifications :
- Education: studies in the field of Electrical Engineering Control Engineering Computer Science Mathematics or comparable
- Experience and Knowledge: in Data Science Machine Learning Non-Linear System identification
- Personality and Working Practice: you excel at sharing your thoughts openly fostering clear communication with others and working independently
- Languages: very good in English or German
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.
You are almost finished with your Bachelors degree and would like to gain some practical experience before embarking on your next academic adventure with a Masters degree Then you fit in perfectly well with our PreMaster Programm! Take a look at our vacancies here.
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
Ozan Demir (Functional Department)
49 0
Matthias Woehrle (Functional Department)
49 8
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
