In the central research division of Robert Bosch GmbH in Renningen you will be part of a team that is working on the motorcycle safety systems of tomorrow. Our shared goal is to reduce the risk of accidents for motorcyclists while maintaining high riding comfort and enjoyment. A key challenge is the precise modeling and estimation of inverse lateral motorcycle dynamics. This makes it possible to determine the necessary control inputs for a desired vehicle state. A new approach is to learn this dynamic using machine learning or deep learning.
- As a part of your Master thesis you will evaluate classical and deep learning-based methods for time-series prediction.
- To this end you will analyze motorcycle dynamics data.
- In addition you will identify suitable modeling approaches implement them in PyTorch and assess the results based on real datasets from test rides.
- We offer you the opportunity to collaborate in an interdisciplinary team with experts in deep learning and rider assistance systems. You will gain access to powerful GPU resources and extensive vehicle dynamics data as well as engage in practically relevant research with direct application in safety-critical systems.
Qualifications :
- Education: Master studies in the field of Computer Science Engineering Natural Sciences or comparable with a strong academic record
- Experience and Knowledge: hands-on experience from relevant projects; good programming skills in Python; experience with deep learning frameworks such as PyTorch or TensorFlow; a basic understanding of systems theory and vehicle dynamics is an advantage
- Personality and Working Practice: you are a team player with a passion for innovation and technology and an analytical and structured working style
- Languages: good in German or English
Additional Information :
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
Alexander Lutzke (Functional Department)
49
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
In the central research division of Robert Bosch GmbH in Renningen you will be part of a team that is working on the motorcycle safety systems of tomorrow. Our shared goal is to reduce the risk of accidents for motorcyclists while maintaining high riding comfort and enjoyment. A key challenge is the...
In the central research division of Robert Bosch GmbH in Renningen you will be part of a team that is working on the motorcycle safety systems of tomorrow. Our shared goal is to reduce the risk of accidents for motorcyclists while maintaining high riding comfort and enjoyment. A key challenge is the precise modeling and estimation of inverse lateral motorcycle dynamics. This makes it possible to determine the necessary control inputs for a desired vehicle state. A new approach is to learn this dynamic using machine learning or deep learning.
- As a part of your Master thesis you will evaluate classical and deep learning-based methods for time-series prediction.
- To this end you will analyze motorcycle dynamics data.
- In addition you will identify suitable modeling approaches implement them in PyTorch and assess the results based on real datasets from test rides.
- We offer you the opportunity to collaborate in an interdisciplinary team with experts in deep learning and rider assistance systems. You will gain access to powerful GPU resources and extensive vehicle dynamics data as well as engage in practically relevant research with direct application in safety-critical systems.
Qualifications :
- Education: Master studies in the field of Computer Science Engineering Natural Sciences or comparable with a strong academic record
- Experience and Knowledge: hands-on experience from relevant projects; good programming skills in Python; experience with deep learning frameworks such as PyTorch or TensorFlow; a basic understanding of systems theory and vehicle dynamics is an advantage
- Personality and Working Practice: you are a team player with a passion for innovation and technology and an analytical and structured working style
- Languages: good in German or English
Additional Information :
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
Alexander Lutzke (Functional Department)
49
#LI-DNI
Remote Work :
No
Employment Type :
Full-time
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