Apply by e-mail for a scientific paper or a job with us. Briefly describe why you are interested in the work and what knowledge you have for it. Attach your current transcript of records if you have it to hand.
Knowledge Management
Would you like to gain practical experience in production technology and contribute to securing knowledge in the long term? Then become part of our team. We are looking for support for the systematic recording and processing of our best practices. With the help of digital tools, you will work with experienced team members to build a central knowledge database that optimises our processes and improves communication between our research areas.
Your tasks:
- Development and implementation of a concept to ensure best practices
- Knowledge acquisition and processing with a wiki, GitHub and a data server
- Creating images and texts with the help of artificial intelligence
- Passing on new information to various departments
Desirable skills and knowledge:
- Independent and structured work
- Knowledge in the field of production technology
- Interest in familiarising yourself with new topics
Your contact person




Optimisation of monitoring limits for fault-prone milling processes
In the EmSim project, you will work with us to analyse failure-prone milling processes. The aim is to identify and classify patterns on the basis of spindle current data. The aim of your work is to develop a decision logic to optimise monitoring limits. The analysis and modeling will be carried out using Python.
Your tasks:
- analysis of existing data sets from machining tests in Python
- development of a decision logic for the adjustment of monitoring limits
- verification of the developed logic
Desirable skills and knowledge:
- interest in machining technologies and data analysis
- initial experience with machine learning methods or interest in familiarising yourself with them
- good programming skills in C# or Python
Your contact person




Quality prediction in manufacturing using artificial intelligence
AI-based prediction of component quality enables cost-effective 100 % quality control in manufacturing. In this study a real production dataset has to be analyzed and different AI models evaluated. The goal is to calculate quality parameters with high accuracy based on features in process signals. The focus of the work is on sequence-based AI approaches, which are particularly suitable for processing time series.
Your tasks:
- programming Python scripts for data analysis and preprocessing
- training and evaluating AI models
Desirable skills and knowledge:
- proficiency in German or English
- independent and structured working style
- strong knowledge in time series analysis with Python
Your contact person




Apply to us anyway. We realise a large number of projects and are constantly working on new production technology topics. We will find the right job for you through personal dialogue.