Detailansicht des Stellenangebots

About this role

You will work on structuring and connecting complex manufacturing data to support product-level sustainability analysis. For this you will work with real industrial data and collaborate with domain experts.

The focus is on:

1. Designing a logical data structure
2. Understanding how different data points relate to each other
3. Enabling data retrieval and analysis through queries

In this thesis, you will develop a structured data approach to organize and manage product-level data to support accurate Product Carbon Footprint (PCF) assessments in semiconductor manufacturing, which requires working with complex and fragmented data.

What you will do

• Analyze existing manufacturing and product-level data to identify structures and relationships.
• Design a structured data model to organize product-relevant data.
• Explore and link data to support product-level carbon footprint analysis.
• Develop queries and small-scale prototypes to test the data model.
• Apply the model to a practical case study to demonstrate functionality and data integration (proof-of-concept).
• Document the design, methodology, and findings in a clear and structured thesis report.
• Present results and insights to stakeholders in a concise and professional manner.
• Navigate uncertainty with analytical thinking and open-mindedness.

What you will need

• Enrolled Master’s student in Data Science, Engineering, Information Systems, or similar
• General understanding and passion for data
• Proven experience with data-oriented programming languages (e.g. Python, or similar) and basic knowledge of querying (e.g., SQL, SPARQL, or similar)
• Experience with relational databases and data processing (e.g. SQL, pandas-based ETL, or similar)
• Strong analytical, structured thinking, and problem-solving skills; able to explore solutions independently and show natural curiosity
• Good communication skills in English (German language skills beneficial) and ability to work independently
• Basic knowledge of graph-based data models (e.g. RDF/SPARQL, or similar) is a plus
• Basic knowledge of semiconductor products is a plus
• Interest in sustainability, life cycle assessments, product carbon footprints, or semiconductor manufacturing