Master Thesis Physically Informed Machine Learning Based System Identification in MEMS Gyroscopesat Bosch

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. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

Are you eager to connect physical insights with advanced AI? We are offering an exciting master's thesis opportunity to work on physically informed machine learning for system identification and performance modeling of MEMS gyroscopes.

• During your assignment, you will construct machine learning (ML) algorithms for system identification and performance prediction of MEMS gyroscopes.
• You will examine MEMS gyroscope data through detailed analysis.
• Furthermore, you will assess physically informed ML in comparison to other architectures.
• Moreover, you will acquire a deep physical understanding of MEMS gyroscopes to optimize your models.
• Finally, you will work with real-world sensor data to validate your findings.

Qualifications

• Education: Master studies in the field ofInformatics, Physics, Engineering or comparable with good grades
• Experience and Knowledge: in data driven parameter identification; knowledge of Python, PyTorch, Pandas, and Probabilistic Modeling; practical experience with hands-on data handling and pipeline construction
• Personality and Working Practice: you excel at structuring your tasks systematically, actively exploring new concepts with curiosity, and driving results with high motivation
• Work Routine: your on-site presence is required
• Languages: fluentin English and good in German

Additional Information

Start: according to prior agreement
Duration: 6 months
It is possible to include a 1-month internship before starting the thesis

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations, job references 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?
Jan Ullmann (Functional Department)
+49 7121 354652
Max Laser (Functional Department)
+49 173 2520609

Work #LikeABosch starts here: Apply now!

#LI-DNI

Videos To Watch

Are you eager to connect physical insights with advanced AI? We are offering an exciting master's thesis opportunity to work on physically informed machine learning for system identification and performance modeling of MEMS gyroscopes.

During your assignment, you will construct machine learning (ML) algorithms for system identification and performance prediction of MEMS gyroscopes.

You will examine MEMS gyroscope data through detailed analysis.

Furthermore, you will assess physically informed ML in comparison to other architectures.

Moreover, you will acquire a deep physical understanding of MEMS gyroscopes to optimize your models.

Finally, you will work with real-world sensor data to validate your findings.

Qualifications Education: Master studies in the field of Informatics, Physics, Engineering or comparable with good grades

Experience and Knowledge: in data driven parameter identification; knowledge of Python, PyTorch, Pandas, and Probabilistic Modeling; practical experience with hands-on data handling and pipeline construction

Personality and Working Practice: you excel at structuring your tasks systematically, actively exploring new concepts with curiosity, and driving results with high motivation

Work Routine: your on-site presence is required

Languages: fluent in English and good in German

Additional Information Start: according to prior agreement

Duration: 6 months

It is possible to include a 1-month internship before starting the thesis

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations, job references 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?

Jan Ullmann (Functional Department)

+49 7121 354652

Max Laser (Functional Department)

+49 173 2520609

Work #LikeABosch starts here: Apply now!

#LI-DNI

Videos To Watch

• Education: Master studies in the field ofInformatics, Physics, Engineering or comparable with good grades
• Experience and Knowledge: in data driven parameter identification; knowledge of Python, PyTorch, Pandas, and Probabilistic Modeling; practical experience with hands-on data handling and pipeline construction
• Personality and Working Practice: you excel at structuring your tasks systematically, actively exploring new concepts with curiosity, and driving results with high motivation
• Work Routine: your on-site presence is required
• Languages: fluentin English and good in German

Start: according to prior agreement
Duration: 6 months
It is possible to include a 1-month internship before starting the thesis

Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations, job references 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?
Jan Ullmann (Functional Department)
+49 7121 354652
Max Laser (Functional Department)
+49 173 2520609

Work #LikeABosch starts here: Apply now!

#LI-DNI

Videos To Watch