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Job Description

In this internship you will develop an AI-assisted pipeline that converts technical documents into structured engineering knowledge. The project focuses on applying machine learning and document understanding techniques to automatically identify, extract, validate, and structure material parameters from supplier documentation. The goal is to build a reproducible data pipeline that transforms unstructured documents into a structured knowledge base used in engineering simulations.

• During your assignment you will develop a document understanding pipeline to identify relevant parameter tables in technical documents.
• You will implement machine learning / LLM-based extraction of material parameters and metadata and design a structured representation of material knowledge including provenance and validation rules.
• Furthermore, you will build automated workflows that convert extracted data into simulation-ready datasets.
• For extracted parameters you will implement quality checks and anomaly detection. You will also explore methods to improve extraction accuracy using ML techniques (prompting, classification, model evaluation).
• Additionally, you will create tools that allow engineers to review and approve extracted information efficiently.
• Finally, you will document the workflow and evaluate the performance of the developed system.

Qualifications

• Education: studies in the field of Electrical Engineering, Physics, Computer Science, Data Science or comparable
• Experience and Knowledge: experience with automation, data processing, LLMs, Python, Git, Linux
• Personality and Working Practice: you approach tasks in a structured manner and develop solutions independently
• Work Routine: your on-site presence is required
• Languages: very good in English

Additional Information

Start: according to prior agreement Duration: 3 - 6 months (confirmation of mandatory internship required) We offer you

• 35 hours/week with flextime
• a permanent contact person who will accompany you during your internship
• a modern working environment, as well as mobile working by arrangement
• the opportunity to become part of our student network students@bosch Stuttgart
• discounts in our company restaurants

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. Work #LikeABosch starts here: Apply now! #LI-DNI