Mandatory Internship in Data Analysis & Machine Learning Model Research for at Bosch Group

Stuttgart, Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

07 Jul, 26

Salary

0.0

Posted On

08 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, MATLAB, Pandas, NumPy, Matplotlib, Seaborn, Machine Learning, Data Analysis, Time-series Analysis, Generative Adversarial Networks, Variational Autoencoders, RNNs, Transformers, TensorFlow, PyTorch, Scikit-learn

Industry

Software Development

Description

Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference. The Robert Bosch GmbH is looking forward to your application! 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. 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 Additional Information Start: according to prior agreement Duration: 3 - 6 months (confirmation of mandatory internship required) Requirement for this internship is the enrollment at university. Please attach your CV, transcript of records, enrollment certificate, examination regulations and if indicated a valid work and residence permit. Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity. Need further information about the job? Lin Shen (Functional Department) +49 711 811 19156 Work #LikeABosch starts here: Apply now! #LI-DNI Legal Entity: Robert Bosch GmbH

Responsibilities

You will analyze large vehicle operational datasets to identify patterns and develop machine learning architectures for powernet simulations. Additionally, you will research state-of-the-art generative models and document your findings to support future model implementation.