Working Student / Internship / Master Thesis - Signal Processing and Machine Learning(m/w/x)

Working Student / Internship / Master Thesis (all genders) - Signal Processing and Machine Learning at Fraunhofer-Institut für Integrierte Schaltungen IIS The **Fraunhofer-Gesellschaft** is one of the world’s leading applied research organizations. 75 institutes develop cutting-edge technologies for our economy and society – more precisely: 32 000 people working in technology, science, administration and IT. They know: If you join Fraunhofer, then you are able to promote change. For themselves, for us and for the markets of today and tomorrow. One institute – countless opportunities. That’s **Fraunhofer Institute for Integrated Circuits IIS**! At the largest Fraunhofer Institute, everything in our specialist departments revolves around research and development projects in the fields of artificial intelligence, microelectronics, sensor systems, data collection, signal processing and transmission. We know that excellent research requires outstanding support. That’s why our administrative staff goes above and beyond to support science in finding the best solutions for our clients and making the world more sustainable. **You are interested in combining research and practices and would like to develop further in the fields of Signal Processing and Machine Learning? Then have a look at our offer!** The division »**Audio and Media Technologies (AME)**« has been making its mark on media digitalization for over 30 years, developing pioneering standards such as MP3, AAC, xHE AAC, and EVS. Motivated by this success, the »**Videocoding**« group focuses on efficient multimedia representation and aims to transfer this know-how to solve new challenges in video coding for better user experiences and higher system efficiency. * **Efficient processes**: You optimize the next generation of image and video processing systems with ML models * **Team support**: You coordinate with engineers and scientists in design and analysis of experimental results. * **Data management**: You manage the analysis and presentation of findings from quantitative and subjective quality tests. * You are currently **studying** machine learning, signal processing, multimedia networks or a related field * You have **working experience** with **Python** and **machine learning frameworks** such as PyTorch and TensorFlow * You are **interested** in **learning-based signal processing for communication systems**. * **Organize your schedule**: Benefit from flexible working hours that are perfectly compatible with your studies. * **Become part of a creative team**: Experience an open and friendly working atmosphere in which your ideas are valued. * **Variety that inspires**: Look forward to divers tasks that inspire and challenge you. * **Shape the future with us**: Take part in application-oriented research and put your theoretical knowledge to practice. * **Innovation that inspires**: Exciting and pioneering projects that make a real difference. We will agree your start date and weekly working hours with you individually (as a working student **12** to **20** hours per week or for full-time internship of at least three months). You can reduce your hours before exams and increase them during semester breaks. You can set your working days flexibly. After your studies, there are attractive opportunities to join the institute on a full-time or part-time basis. We would be happy to offer you the opportunity to write a master's thesis in cooperation with us in the above-mentioned subject area. The thesis will be assigned and carried out in accordance with the rules of your university. For this reason, please discuss the thesis with a professor who can advise you over the course of the project. We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled people are given preference if they are equally qualified.