Modern high-speed electronic systems rely on accurate material parameters such as dielectric constant (Dk) and loss tangent (Df). These parameters are typically published by suppliers in large technical datasheets and lineup documents. Extracting and maintaining this information in a structured form for engineering use is currently a manual and time-consuming process.
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.
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?
Torben Wendt (Functional Department)
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Veröffentlichungsdatum:
12 Mär 2026Standort:
SchwieberdingenEinsatzort:
Robert Bosch GmbH, Postfach 30 02 20, 70442 Stuttgart, DeutschlandTyp:
VollzeitArbeitsmodell:
Vor OrtKategorie:
Erfahrung:
2+ yearsArbeitsverhältnis:
Angestellt
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