Mandatory Internship Data Analysis & ML Model Research for Automotive Syste at Bosch Group

Stuttgart, Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

01 Jul, 26

Salary

0.0

Posted On

02 Apr, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

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

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 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 will enhance the performance of a future generative model. Gain experience in conducting a comprehensive literature review and comparative analysis of state-of-the-art machine learning models for synthetic time-series generation (e.g., Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), RNNs, Transformers). Lastly you will conclude your internship by preparing a detailed report and presentation that summarizes your data findings and provides a well-reasoned recommendation for the most promising ML model architecture and data strategy to be pursued in the next phase. Qualifications Education: studies in the field of Electrical Engineering, Data Science, Computer Science, Statistics, or comparable with a strong analytical focus Experience and Knowledge: good programming skills in Python or Matlab and experience with data analysis libraries such as Pandas, NumPy, and Matplotlib/Seaborn; A solid theoretical understanding of data analysis techniques and fundamental machine learning concepts; A 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 able to analyze situations in depth and investigate complex relationships; you approach problems in a structured and methodical way to find effective solutions; you work independently and always document your results clearly and comprehensibly Work Routine: office attendance required Languages: business fluent in English or/and 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 vehicle operational data to develop a machine learning-based load profile generator for powernet simulations. Additionally, you will conduct literature reviews and research to recommend optimal ML architectures for synthetic time-series generation.