Thesis in Transfer Learning for Data-Based Control of Hydraulic Cylinders

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

Zusammenfassung

Bosch Group propose un stage en Recherche et Développement pour le développement de systèmes de contrôle d'apprentissage destinés aux excavatrices. Les candidats auront l'occasion d'explorer des méthodes novatrices tout en travaillant dans un environnement axé sur la diversité et l'inclusion. Ce stage de 6 mois s'adresse aux étudiants en Ingénierie, offrant une expérience pratique précieuse avant l'obtention de leur Diplôme.

Qualifikationen

• Étudiant en dernière année de licence ou début de master souhaité.
• Très bon niveau en anglais ou allemand est requis.

Aufgaben

• Explorer des méthodes pour transférer des connaissances et des paramètres de modèles.
• Développer des stratégies pour réduire la charge computationnelle des ajustements de modèle.
• Créer des méthodes pour la validation en ligne des adaptations de modèles.

Kenntnisse

Data Science

Machine Learning

Non-Linear System Identification

Communication

Independent Work

Ausbildung

Études en Ingénierie Électrique, Ingénierie de Contrôle, Informatique, Mathématiques ou similaire

Jobbeschreibung

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.

The Robert Bosch GmbH is looking forward to your application!

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 Electrical Engineering, Control Engineering, Computer Science, Mathematics, or comparable
• Experience and Knowledge: Data Science, Machine Learning, Non-Linear System Identification
• Personality and Working Practice: You excel at sharing your thoughts openly, fostering clear communication, 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 enrollment at university. Please attach your CV, transcript of records, examination regulations, and if indicated, a valid work and residence permit.

If you are almost finished with your Bachelor's degree and want to gain practical experience before pursuing a Master's degree, then you fit perfectly with our PreMaster Program! Check out our vacancies here.

Diversity and inclusion are core to our 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]

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