Bosch Gruppe

  • Schmidt und Stephan GmbH Sachverständigenbüro für KFZ-Schäden und -Bewertungen, Gerberstraße, Winnenden, Stuttgart, Germany
  • https://www.bosch.de/

The Bosch Group is a leading international technology and service company with around 410,000 employees worldwide (as at 31.12.2018). In the financial year 2018, it generated sales of 78.5 billion euros. The activities are divided into the four business units Mobility Solutions, Industrial Technology, Consumer Goods and Energy and Building Technology. As a leading provider of the Internet of Things (IoT), Bosch offers innovative solutions for Smart Home, Smart City, Connected Mobility and Industry 4.0. With its expertise in sensors, software and services and its own IoT cloud, the company is able to offer its customers networked and cross-domain solutions from a single source. The strategic goal of the Bosch Group are solutions for connected life. With innovative and inspiring products and services, Bosch is improving people's quality of life worldwide. Bosch offers "technology for life".

03/04/2026
Full time
Bosch Gruppe Stuttgart, Weilimdorf, Germany
Tasks This internship is the foundational first phase of an innovative project aimed at developing a machine learning-based "Load Profile Generator" for vehicle E/E powernet simulations. Your mission is to analyze our rich datasets of vehicle operational data and conduct the critical research needed to select the optimal ML architecture for this task. The results of your internship will directly inform and enable a subsequent Master's thesis project focused on model implementation and integration. During your internship you will dive deep into large time-series datasets from vehicle measurements to understand underlying patterns, distributions, and characteristics of vehicle power consumption. Furthermore you will develop as well as implement robust scripts and workflows for cleaning, transforming, and preparing the raw data into a structured format suitable for ML model training. You will identify and create meaningful features from the time-series data that...