Thesis on Improving Data Quality for Learning-Based Controllers

Robert Bosch Group; Renningen; Vor Ort; EUR 40.000 - 60.000; Vollzeit

Zusammenfassung

Join a forward-thinking company that is shaping the future with innovative technologies! This exciting thesis opportunity focuses on improving data quality for learning-based controllers in hydraulic systems. You will engage in hands-on testing and development, working with real excavators and simulations. The role promises a collaborative environment where your contributions will directly impact advanced excavator assistance functions. If you're passionate about technology and eager to make a difference, this is the perfect opportunity for you!

Qualifikationen

• Enrollment at university is required for this thesis.
• Experience in Electrical Engineering or Computer Science is advantageous.

Aufgaben

• Determine operational space for learning-based control of hydraulic cylinders.
• Develop measures to reduce computational load for reliable lifelong learning.

Kenntnisse

Electrical Engineering

Computer Science

Mathematics

Communication Skills

Ausbildung

Bachelor's in Electrical Engineering

Bachelor's in Computer Science

Bachelor's in Mathematics

Jobbeschreibung

Thesis on Improving Data Quality for Learning-Based Controllers

• 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.

The Robert Bosch GmbH is looking forward to your application!

At Bosch Corporate Research in Renningen, we are working on learning control of hydraulic cylinders for advanced excavator assistance functions.

• During your thesis, you will determine the relevant operational space for learning-based control of hydraulic cylinders for different excavators.
• You will use new concepts to detect distributional shifts of excavators and collect missing data.
• In addition, you will develop measures to reduce the computational load of V&V concepts to enable reliable lifelong learning.
• Last but not least, you will test the developed concepts using real excavators and/or simulations with real excavator data.

• Education: studies in Electrical Engineering, Electronics, Computer Science, Mathematics, or comparable fields.
• Experience and Knowledge: experience in Electrical Engineering, Electronics, Computer Science, or Mathematics is advantageous.
• Personality and Working Practice: you are an open and communicative person who can work independently.
• Languages: fluent in German or English.

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is enrollment at university. Please attach your CV, transcript of records, examination regulations, and if applicable, a valid work and residence permit.

Diversity and inclusion are integral to our corporate culture. 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): [phone]
Matthias Woehrle (Functional Department): [phone]