Mandatory Internship in Data Analysis & Machine Learning Model Research for Automotive Systems

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The Robert Bosch GmbH is looking forward to your application!

This mandatory internship marks the first phase of an innovative project to develop a machine learning (ML)‑based Load Profile Generator for vehicle E/E powernet simulations. You will analyze extensive vehicle operational datasets and conduct key research to identify the most suitable ML architecture. The outcomes will directly pave the way for a follow‑up Master’s thesis focused on model implementation and integration.

Responsibilities

You will dive deep into large time‑series datasets from vehicle measurements to explore underlying patterns, distributions, and characteristics of vehicle power consumption.; As part of your work, you will develop and apply robust scripts and workflows to clean, transform, and prepare raw data for use in machine learning (ML) model training.; You will identify and engineer meaningful features from time‑series data to support and enhance the performance of a future generative model.; Through a comprehensive literature review and comparative analysis, you will research state‑of‑the‑art machine learning approaches for synthetic time‑series generation, including models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), RNNs, and Transformers.; At the end of the internship, you will summarize your findings in a detailed report and presentation, providing a well‑reasoned recommendation on the most promising ML model architecture and data strategy for the next project phase.

Requirements

Education: studies in the field of Engineering, Data Science, Computer Science, Statistics or a comparable field with a strong analytical focus; Experience and Knowledge:; good programming skills in Python or MATLAB and knowledge of data analysis libraries such as Pandas, NumPy, and Matplotlib/Seaborn; solid theoretical understanding of data analysis techniques and fundamental machine learning (ML) concepts; keen interest in researching and comparing different algorithmic approaches,; first-hand experience with ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) is a plus; familiarity with time-series data analysis; Personality and Working Practice: you are a person with a strong analytical and investigative mindset, a structured and methodical approach to problem-solving, and the ability to work independently and document findings clearly; Work Routine: your on-site presence is required; Languages: fluent in English and/or German