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

Descripción del puesto:

Job Description

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

Requerimientos del candidato/a:

Qualifications

* 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