Environment

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. There is the possibility to write a thesis.

Possible Tasks within this Role

• Research literature in the field of supervised learning (regression) and statistical modeling to predict time series data

• Identify and adapt best practices for accurate predictions according to project requirements.

• Create and implement Python scripts to process data, analyze, visualize, and customize selected methods

• Conducting and evaluating experiments with the implemented methods and analyzing the results, including strengths and weaknesses

Qualification requirements

• Students of engineering, computer science, or comparable fields of study

• Very good performances

• Interest in passive vehicle safety and the application of AI methods in real-world scenarios

• Basic knowledge of relevant machine learning frameworks such as PyTorch, TensorFlow, and Bitbucket/GIT is an advantage for version control

• Strong programming skills in Python

• German and English language level C1

• Independent work, problem solving, structured working style

The following documents must be submitted with the application

• 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