Master Thesis in Machine Learning Strategies to Enhance Performance of Contact Simulations
Master Thesis in Machine Learning Strategies to Enhance Performance of Contact Simulations
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Job Description
Join our innovative team and collaborate with Bosch researchers to develop highend simulation tools used in the design and development of critical components for market applications.
- During your thesis you will gain deep insights into our proprietary simulation software for contact dynamics. Your primary focus will be on enhancing our models and methods to increase the speed quality and efficiency of the simulations using machine learning (ML) methods.
- You will develop algorithms and models to improve performance scalability and efficiency.
- Furthermore you will analyze and interpret simulation output datasets to extract meaningful information for training your algorithms.
- You will utilize Machine Learning techniques ranging from Gaussian Optimization to Neural Networks to develop comprehensive design models based on extensive simulation data.
- Finally you will test and demonstrate your improvements on real design challenges.
Qualifications :
- Education: Master studies in the field of Engineering Computer Science Applied Mathematics or comparable with good grades
- Experience and Knowledge: proficiency in programming languages such as Python; strong background in AI machine learning and optimization methods
- Personality and Working Practice: you are a selfstarter who works effectively both independently and as part of a team; you identify challenges proactively propose innovative solutions and have a structured organized approach to research combined with excellent analytical and critical thinking skills
- Enthusiasm: for machine learning and programming
- Languages: very good in English or German
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
Cesar Pastor (Functional Department)
49 2
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
