Student Assistant
vor 2 Wochen
**Predictive Quality - Machine Learning for Production Engineering**:
**Contact**:
**Name**:
Simon Cramer
**Telephone**:
- workPhone- +49 241 80-28394***:
**Institution**:
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 We Offer**:
The position is to be filled at the earliest possible date and offered for a fixed term of initially 6 months.
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.
- Application
- Number:
- V000004576- Application deadline:
- 30/04/2023- Mailing Address:
- RWTH Aachen University
- Lehrstuhl für Fertigungsmesstechnik und Qualitätsmanagement
- Simon Cramer
- Campus Boulevard 30
- 52074 Aachen
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