Volkswagen
In the Vehicle Safety Test team, we are responsible for the entire scope of testing relating to active and passive safety. This includes the development of necessary test equipment, measuring tools, analysis methods, software tools, IT systems and databases. Due to the large amount of measurement data generated, it is necessary to be able to carry out analyses without human intervention. To this end, AI algorithms are trained and continuously updated with newly entered data in order to generate the most optimized and meaningful evaluations possible. The assignment is now possible with a duration of 6 months. This internship is to be completed full-time.
Conduct literature research in the field of supervised learning (regression) and statistical modeling for time series prediction
Identify and adapt the best methods for accurate predictions according to the project requirements
Create and implement Python scripts for data processing, analysis, visualization, and adaptation of selected methods
Conduct and evaluate experiments with the implemented methods and analyze the results, including strengths and weaknesses
Students in the field of engineering, computer science or related fields
Very good performances
Working independently, solving problems, structured working style
Interest in passive vehicle safety and the application of AI methods in real-world scenarios
German and English language level C1
As well as strong programming skills in Python
Initial experience with relevant machine learning frameworks such as PyTorch, TensorFlow, and Bitbucket/GIT is an advantage for version control
Curriculum vitae
Current certificate of enrolment
Current transcript of records
In the case of a compulsory internship, an additional certificate from the university
Work permit for non-EU citizens
Contact person for this posting: Brigitte Adam-Huth
In the Vehicle Safety Test team, we are responsible for the entire scope of testing relating to active and passive safety. This includes the development of necessary test equipment, measuring tools, analysis methods, software tools, IT systems and databases. Due to the large amount of measurement data generated, it is necessary to be able to carry out analyses without human intervention. To this end, AI algorithms are trained and continuously updated with newly entered data in order to generate the most optimized and meaningful evaluations possible. The assignment is now possible with a duration of 6 months. This internship is to be completed full-time.
Conduct literature research in the field of supervised learning (regression) and statistical modeling for time series prediction
Identify and adapt the best methods for accurate predictions according to the project requirements
Create and implement Python scripts for data processing, analysis, visualization, and adaptation of selected methods
Conduct
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