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In the Vehicle Safety Test Technology team, we are responsible for the entire scope of active and passive safety experiments. This also includes the ongoing development of the test facilities, measuring equipment, analysis methods, software tools, IT systems and databases required for this purpose. Due to the large amount of measurement data generated, it is necessary to be able to carry out analyses without human intervention. For this purpose, AI algorithms are trained and constantly updated with newly entered data so that the most optimized and meaningful evaluations possible are created. There is the possibility to write a thesis after this internship.
Job Responsibility:
Conduct research on condition monitoring of mobile robots, use of time series data with a special focus on physics-informed models
Identify methods that best meet the project's needs and offer high potential for accurate predictions
Develop Python scripts for data processing, analysis, and visualization
Implement and adapt methods in Python, and conduct experiments with mobile robots for evaluation
Analyzing and interpreting the results, evaluating the methods and presenting to the supervisor and research team
Requirements:
Students in engineering, computer science, data science or comparable fields of study
Very good academic performance
Good programming skills in Python, including the application of OOP principles
Basic knowledge of relevant ML frameworks such as PyTorch or TensorFlow, as well as data processing libraries such as Pandas
Interest in integrating technical system understanding and data-based analysis
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