Master Thesis Physically Informed Machine Learning Based System Identification in MEMS Gyroscopes
Bosch · Reutlingen, BW, Germany
What the posting says
Each fact was read from this posting and checked:Statedin the posting, quotedConflictsposted two ways, both shownNot mentionedwe looked; it is not there
- Employment typeStated
- Full time
Not mentioned in this posting
We read the whole posting. These are not in it. Not a no: ask the recruiter, in your own words.
- PayNot mentioned
- The posting does not say
- Work arrangementNot mentioned
- The posting does not say
- ShiftNot mentioned
- The posting does not say
- ExperienceNot mentioned
- The posting does not say
- EducationNot mentioned
- The posting does not say
- Visa sponsorshipNot mentioned
- The posting does not say
Requirements, each with its sentence from the posting
These are the employer’s words. The index does not judge whether you meet them.
- No requirement has been itemized for this posting yet. The employer’s words are in the full posting below.
Read the full posting as the employer wrote it
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 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: March/April 2027
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
The posting text is reproduced as the employer wrote it; the index adds nothing to it.