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

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.

• 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.