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Working Student – Machine Learning for Audio Compression (all genders)


Job Location:

Erlangen - Germany

Monthly Salary: Not provided by the employer
Posted: 11 July 2026 (30+ days ago)
Application Deadline: 8 October 2026
Vacancies: 1 Vacancy

Job Summary

As the largest institute within the Fraunhofer-Gesellschaft we have been conducting research for over 30 years in diverse fields such as artificial intelligence microelectronics sensor systems data collection signal processing and transmission. Talents from various engineering and natural science disciplines will find a wide range of exciting research topics under a single roof. A few highlights that reflect our innovative strength: Neuromorphic computing 5G/6G mobile communication technologies generative AI in speech and signal processing and X-ray imaging for e-mobility. And thats just the beginning! Which sustainable solutions will you find with us

You are interested in advancing your skills in Machine Learning while working on audio processing and compression technologies
Then have a look at our offer!

The department AME-A develops innovative codecs for the transmission of audio signals. Our solutions are suitable for a wide range of signalsfrom speech and music to immersive 3D audioacross a broad spectrum of bitrates and are already being successfully used by major providers in the streaming and broadcast industries.

  • Machine Learning: You implement neural network components and architectures develop training and evaluation pipelines and analyze model performance to continuously improve results.
  • Audio & Data Processing: You create and prepare training datasets process audio data using signal processing techniques and support the design and evaluation of listening tests.
  • Development & Optimization: You contribute to the development of AI-based audio compression solutions by training and evaluating models optimizing workflows and improving model performance.
  • You are currently studying Computer Science Electrical Engineering Data Science Artificial Intelligence or a related field.
  • You have a basic understanding of Machine Learning / Deep Learning and are familiar with signal processing concepts and audio data (e.g. sampling rate FFT spectrograms).
  • You have strong programming skills in Python and enjoy implementing and experimenting with machine learning models.
  • You are familiar with Git and have basic experience working in a Linux environment.
  • You are interested in audio compression and motivated to contribute to innovative AI-based research and development projects.
  • 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 8 to 20 hours per week for at least one semester). You can reduce your hours before exams and increase them during semester breaks. We are looking for someone interested in a long-term collaboration and please note: the position is on-site only. 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. You can flexibly determine the working days of your fixed-term employment contract.

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.

Then apply now and make the difference! (PDF: Cover Letter Resume Certificates).

Do you have questions about the application process Our recruiter Svenja Hofmann will be happy to assist you.
E-Mail Address:

Location: Erlangen


Required Experience:

Unclear Seniority