Machine Learning

Want to know what holds your car together? Modern cars contain tens of thousands of solder joints in up to 150 electronic control units. Under the most adverse operating conditions, the solder joints in cars are supposed to last for years and ensure that, for example, LED headlights or sensors for autonomous driving take us safely to our destination. In our current research project MaWis-KI, you will find out how solder joints change during their lifetime. Ultrasonic microscopy, X-ray images, thermomechanical analyses and FEM simulations are available as data sources. You combine this data with the latest machine learning methods to develop sustainable electronics for the vehicles of the future. In an agile and young research team, you help to research on the lifetime prediction of electronic components with machine learning. Your focus will be on building the infrastructure and pipeline for data acquisition, storage, and pre-processing. You will learn how machine learning projects are professionally implemented and you will have the opportunity to develop yourself and your skills. We are interested in a long-term cooperation and are extremely flexible regarding timing of internships and working student activities. These tasks interest you You will support one of our research projects with further partners from science and industry and help to develop future innovation topics. You will work in an agile and young research team with scientists, PhD students and students. You will independently implement tasks for data acquisition and processing in machine learning. As a full member of our team, you are involved in project work and coordination meetings. You can get involved in our XITASO research community and exchange ideas with our AI experts. That makes you stand out You are studying a STEM subject (e.g., computer science, mathematics, physics, etc.) or have a comparable computer science related educational background. You have already gained some practical experience programming in Python and with relevant libraries (e.g., TensorFlow, PyTorch, pandas, numpy). You have a good understanding of machine learning. You have basic knowledge of source code management and databases. You are interested in new technologies.