Have you worked with ML models before and are interested in applying and deepening your skills by using them on data of MEMS ultrasonic transducers? Join our team in Munich and contribute to enabling innovative applications of ultrasound! This position will allow you to satisfy your curiosity about machine learning algorithms and MEMS ultrasonic sensors and boost your career at the same time. Don't hesitate to apply! In your new role you will:

• Familiarize yourself with existing models and choose the best suited model for Infineon Ultrasonic MEMS data
• Adapt existing models for Infineon Ultrasonic MEMS data
• Adjust model parameters and compare the model to other existing models and methods

You are best equipped for this task if you:

• Study Informatics, Computer Science or a related subject
• Already gained experience with data processing and ML algorithms mainly in Python
• Bring along knowledge of deep learning libraries e.g. Pytorch, TensorFlow, Keras and basic knowledge in Linux
• Speak English fluently

Please attach the following documents to your application:

• CV in English
• Certificate of enrollment at university
• Latest grades transcript (not older than 6 months)
• High school report

Part of your life. Part of tomorrow.

Infineon is a world leader in semiconductor solutions that make life easier, safer, and greener. Our solutions for efficient energy management, smart mobility, and secure, seamless communications link the real and the digital world.

The Business Excellence Department contributes to Infineon's vision of a high-performance company. This goal is supported by the systematic use of quality management principles and tools in business management, with the goal of improving performance based on the principles of customer focus, stakeholder value, and process management.

• The term gender in the sense of the General Equal Treatment Act (GETA) or other national legislation refers to the biological assignment to a gender group. At Infineon we are proud to embrace (gender) diversity, including female, male and diverse.