Master Thesis AI-based Sensorless Edrive Control

Robert Bosch Group; Renningen; Vor Ort; EUR 10.000 - 15.000; Vollzeit

Zusammenfassung

Robert Bosch Group in Renningen invites applications for a Master Thesis on AI-based sensorless Edrive control. The project focuses on using neural networks to learn control and estimation tasks for electric drives in a hybrid work setup.

You will model, simulate, and analyze motor drive systems, compare ML approaches, and develop AI-based control architectures. Excellent graduate students in Cybernetics, Engineering, or CS are encouraged to apply.

Qualifikationen

• Master’s level studies with good grades in a relevant field.
• Profound knowledge of control engineering and machine learning.
• Experience with Python or MATLAB.
• Analytical, structured, and highly autonomous working style.
• Hybrid work setup and English proficiency.

Aufgaben

• Gain understanding of physical models of electric drives and the simulation environment.
• Analyze state-of-the-art ML and neural network approaches for sensorless drive control.
• Develop novel AI-based control architectures, modular and end-to-end designs.
• Implement, test, and validate control algorithms using high-fidelity simulations.
• Document methodology, analyze results, and present findings to the development team.

Kenntnisse

Control Engineering

Machine Learning

Python

MATLAB

English

Ausbildung

Master studies in Cybernetics, Engineering, Mathematics, Computer Science or comparable

Tools

Python

MATLAB

Jobbeschreibung

Master Thesis AI-based Sensorless Edrive Control

• 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.

TheRobert Bosch GmbHis looking forward to your application!

Electric drives are at the heart of modern mechatronics, and the industry is rapidly moving toward sensorless control to estimate rotor positions using software rather than physical sensors. This thesis explores how Artificial Intelligence and Neural Networks can revolutionize traditional control architectures. The goal is to investigate and design advanced neural network concepts that learn control and estimation tasks. By mapping system measurements to control states, this research aims to pave the way for the next generation of intelligent, software-defined electric drive control.

• During your thesis you will gain a solid understanding of the physical models of electric drives (e.g., electric machines, inverters) and the simulation environment.
• You will analyze state-of-the-art machine learning and neural network approaches applied to sensorless control of electric drives.
• Furthermore, you will develop novel AI-based control architectures, exploring both modular and end-to-end neural network designs.
• You will implement, test, and validate your control algorithms using high-fidelity electric drive simulation models.
• Finally, you will document your methodology, analyze the results, and present your findings to the development team.

• Education: Master studies in the field of Cybernetics, Engineering, Mathematics, Computer Science or comparable with good grades
• Experience and Knowledge: profound knowledge of control engineering and machine learning; experience in Python or MATLAB
• Personality and Working Practice: you excel at analyzing complex problems, structuring your tasks systematically, while driving results with a highly autonomous working style
• Work Routine: we offer you the opportunity to work in a hybrid setup
• Languages: very good in English

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?
FelixBerkel (Functional Department)
[phone]

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