Student Assistant (f/m/non-binary)

Predictive Quality – Machine Learning for Production Engineering



Simon Cramer


+49 241 80-28394



Lehrstuhl für Fertigungsmesstechnik und Qualitätsmanagement

Our Profile

Today's challenges in production engineering are no longer solved by automation systems and downstream quality management. Data-driven systems enable adaptive automation, and the embedding of quality management into the process chains. This is the focus of the Model-based Systems department's research. The research group Risk-based Process Control supports companies in the development and implementation of novel sensor- and data-based solutions for production. The range of services extends from classic analysis and consulting projects to the technical development of individual sensor solutions.

Your Profile

  • You are studying engineering or one of the STEM degree programs
  • Programming skills required (e.g. Python or R)
  • Knowledge of statistics and machine learning preferred
  • You are eager to learn
  • Good communication skills in English (German is a plus)

Your Duties and Responsibilities

The work as a student assistant in our department is diverse and dependent on ongoing research and industrial projects. Long-term (> 1 year) employment at our institute is preferable. As a member of the research group Risk-based Process Control, you will support research and industry projects around predictive quality, the data-based quality prediction using machine learning. For this purpose, you support implementing machine learning and data processing methods. Typical tasks you will support:

  • Data acquisition, storage, and processing, based on DjangoREST, SQL, InfluxDB, MQTT, …
  • Implementation of machine learning algorithms, e.g. Transformer, BNN, GRU
  • Data analysis and preprocessing with common statistical and machine learning methods
  • Application of MLOps, i.e., ArgoWorkflows
  • Documentation of code and results, e.g. in Gitlab

Your tasks will be in line with the department's focus areas. Active participation in current research and industry projects as well as research on cutting-edge issues in the academic and industrial context is encouraged. How to apply? Send a brief motivational letter answering the following questions:

  • How will ML/ AI influence the area of production engineering?
  • What is the biggest challenge when applying ML/ AI to the manufacturing industry?

preferably by e-mail to (maximum 600 words). Please include a brief CV (maximum 1 page) as well.

What We Offer

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 initially 6 months.
Provided the candidate performs their work well, an extension is possible and indeed desirable.
This is a part-time contract position.
The standard weekly hours will be 12-19 hours.
The salary is based on the RWTH Guidelines for Student and Graduate Assistants.
The position corresponds to a pay grade of 12,00 € per hour.

About us

RWTH is a certified family-friendly University. We support our employees in maintaining a good work-life balance with a wide range of health, advising, and prevention services, for example university sports. Employees who are covered by collective bargaining agreements and civil servants have access to an extensive range of further training courses and the opportunity to purchase a job ticket.
RWTH is an equal opportunities employer. We therefore welcome and encourage applications from all suitably qualified candidates, particularly from groups that are underrepresented at the University. All qualified applicants will receive consideration for employment and will not be discriminated against on the basis of national or ethnic origin, sex, sexual orientation, gender identity, religion, disability or age. RWTH is strongly committed to encouraging women in their careers. Female applicants are given preference if they are equally suitable, competent, and professionally qualified, unless a fellow candidate is favored for a specific reason.
As RWTH is committed to equality of opportunity, we ask you not to include a photo in your application.
You can find information on the personal data we collect from applicants in accordance with Articles 13 and 14 of the European Union's General Data Protection Regulation (GDPR) at

Application deadline:30/04/2023
Mailing Address:RWTH Aachen University
Lehrstuhl für Fertigungsmesstechnik und Qualitätsmanagement
Simon Cramer
Campus Boulevard 30
52074 Aachen
Applicants are invited to submit their applications via email. For data protection reasons, however, we recommend sending applications via mail.