Your mission You will be investigating new methods to extend the field of possibilities for tir e wear estimation, especially for fleet management. Your main responsibility will be to adapt a Virtual Sensor to work on low frequency data as part of an exploration activity. Key Responsibilities Collaborate hand-in-hand with Data-Scientists to support the development of a cutting-edge automotive solution. Understand and modify an existing approach to adapt to challenges from low frequency time series typically available for fleet monitoring. Imagine, implement and evaluate different approaches for this Virtual Sensor. Contribute to an exploratory phase of a Virtual Sensor to shape a new market positioning. Your profile Bachelor or Master student in Mechatronics or Mechanical Engineering, with knowledge of vehicle dynamics and signal processing. Proficiency in Matlab / Simulink and Python for data analysis and scripting. Strong analytical and problem-solving mindset. Interest in innovative automotive technologies development. Experience with time-series data analysis is a plus. About us COMPREDICT is driving the paradigm shift towards software-defined vehicles, offering next-level solutions for sustainable mobility. Founded in 2016 in Darmstadt, Germany, by Dr. Rafael Fietzek and Dr. Stéphane Foulard, our focus lies in developing Virtual Sensors for Mobility to optimize vehicle design, usage, and maintenance. We partner with and serve automotive manufacturers and Tier1 companies worldwide. With a diverse team of 35 members from 10 different nations, we are on an exciting journey of growth and innovation.