Master Thesis Student (m/f/d) - Data Modeling for Product Carbon Footprint in Semiconductor Manufacturing

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 cooperation 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 want 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; let's explore solutions 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 What you want to gain Hands-on experience with real product-level industrial data. Exposure to complex semiconductor manufacturing processes and cross-team collaboration. Development of strong analytical, problem-solving, and stakeholder management skills. Experience working in a new and exploratory area, shaping future work will proceed. Potential opportunities to continue the project or join the company after graduation.

Conditions Full time fixed-term contract Attractive remuneration and a bonus for a very good final thesis Location: Hamburg Talent acquisition based on Nexperia vacancies is not appreciated. Nexperia job adverts are Nexperia copyright © material and the word Nexperia® is a registered trademark. D&I statement As an equal-opportunity employer, Nexperia values diversity not just because it is the right thing to do but because various teams perform better. We are dedicated to being inclusive, and a proof point of this dedication is that we were the main partner of the very first Dutch Paralympic Team NL House during the Paris 2024 Paralympic Games. Our recruitment process is inclusive and accessible to all, and we consider all applicants fairly, as well as providing a work environment and reasonable adjustments where safe requested. In addition, we offer our colleagues the possibility to join employee resource groups search as the Pride Network Group or global and local Women's groups. Nexperia is committed to increasing women in management position to 30% by 2030. .