Student Assistant (f/m/non-binary) – Digitization and Data Analysis in Production

The Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University has stood for decades worldwide as a guarantee for successful and forward-looking research and innovation in the field of production technology. The "Digital Production and NC Technology" group at the Chair of Machine Tools, held by Prof. Dr.-Ing. Christian Brecher, focuses on the integrative connection of real and digital systems along the product development process.Through the systematic aggregation, analysis, and interpretation of planning, process, and inspection data, we create transparency in complex manufacturing environments. Using data science and artificial intelligence, we identify optimization potential and develop models for the production of tomorrow. To strengthen our team, we are looking for motivated students who want to combine theory and practice in exciting projects. We offer our student assistants:

•  Interesting tasks in applied research and development related to the topic of digitization in production
• Active participation in industrial and research projects
• Opportunities to write a scientific thesis
• Flexible working hours

• Currently enrolled in a degree program in Computer Science, Industrial Engineering, Mechanical Engineering, or similar engineering sciences
• Interest in digitalization, data analysis, and/or lightweight construction
• Very good programming skills in Python
• Experience with SQL databases
• Basic knowledge of or strong interest in AI topics
• Very good German or English skills, both written and spoken
• Initiative and a structured way of working 

The focus of this position is supporting a research project on quality analysis of lightweight components, in which process and inspection data are systematically evaluated to identify quality deviations at an early stage and derive potential for optimization. Possible activities include, among others:

• Setting up and maintaining SQL databases for the structured storage of process and quality data from test series on lightweight components
• Support in the development of automated process data preparation, analysis, and visualization
• Applying and assistance with further development of data-driven (AI) models for quality prediction and defect classification
• Independent research work
• Support in day-to-day operations

The successful candidate will be employed as a student assistant.
The position is to be filled at the earliest possible date and offered for a fixed term of 12 months.
There is an option to extend the contract. Longer-term employment is desirable.
This is a part-time contract position.
The standard weekly hours will be 7-19 hours.
The salary is based on the RWTH Guidelines for Student and Graduate Assistants.
The position corresponds to a pay grade of 15.20 euros per hour.

RWTH is proud to be certified as a family-friendly university. As part of our Campus Health initiative, RWTH offers a wide range of health, counseling, and prevention services, including university sports. Employees under collective bargaining agreements and civil servants also benefit from a comprehensive continuing education program and the option to purchase a job ticket for convenient commuting.
RWTH is an equal opportunities employer. We welcome and encourage applications from all qualified candidates and make employment decisions without regard to national or ethnic origin, sex, sexual orientation, gender identity, religion, disability or age. RWTH is especially committed to supporting the advancement of women in academia. When women are underrepresented in a university unit, preferential consideration will be given to female applicants among candidates of equal merit and professional standing, unless compelling reasons favor another individual.
In keeping with RWTH’s commitment  to equality of opportunity, we ask that you do not include a photo with your application.
Please refer to https://www.rwth-aachen.de/applicant-privacy-policy for information about how we collect and process applicant data in accordance with Articles 13 and 14 of the European Union’s General Data Protection Regulation (GDPR).