Intern / Master Thesis (all genders) - Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets

Intern / Master Thesis (all genders) - Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets at Fraunhofer-Institut für Integrierte Schaltungen IIS | softgarden

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Intern / Master Thesis (all genders) - Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets

Part Time

Hybrid

Am Wolfsmantel 33, 91058 Erlangen

Without Professional Experience

1 / 3 / 26

The Fraunhofer-Gesellschaft   currently operates 76 institutes and research institutions throughout Germany and is the world’s leading applied research organization. Around 32 000 employees work with an annual research budget of 3.4 billion euros. The Fraunhofer Institute for Integrated Circuits IIS, located in Erlangen, is the largest institute of the Fraunhofer-Gesellschaft with more than 1 200 employees.

Conventional digital communication systems rely on Shannon’s separation theorem, which states that optimal performance can be achieved by independently designing source and channel coding components. However, this principle holds only asymptotically for infinitely long packets. In modern low-latency and short-packet communication scenarios, such as real-time multimedia transmission, this separation results in performance loss due to several factors :

• The optimality of the separation theorem breaks down for short block lengths.
• Real-world source coding does not yield ideally independent and identically distributed bitstreams.

To overcome these limitations, recent research explores generative AI-based transmission schemes. For instance, demonstrates the effectiveness of generative AI in communication tasks

, while proposes a variational autoencoder (VAE) architecture that jointly encodes and modulates images, outperforming state-of-the-art separate digital solutions

You are passionate about innovation and eager to tackle fundamental challenges in future communication systems? Then have a look at our offer!

Here’s how you will make a difference

The position is offered under the »Broadband and Broadcasting« department. In the project that this thesis will be part of, we design machine learning based signal processing for physical layer communication.

This thesis provides an exciting opportunity to contribute to cutting-edge research at the intersection of machine learning and communication engineering. The work will be conducted in close collaboration with our R&D team and may lead to publication in a reputable venue.

What you bring to the table

What you can expect

We will agree your start date and weekly working hours with you individually (for an internship 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. You can flexibly determine the working days of your fixed-term employment contract.

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.

Interested? Join our team and work with us on the technology of tomorrow!

Ready for change? Then apply now and make the difference! (PDF : Cover Letter, Resume, Certificates).

Do you have questions about the application process? Our recruiter Anne Weber will be happy to assist you : Phone +49 9131 776-1678

Fraunhofer-Institute for Integrated Circuits IIS

www.iis.fraunhofer.de / en

[1] N. Van Huynh et al., "Generative AI for Physical Layer Communications : A Survey," in IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 3, pp. 706-728, June 2024

[2] Y. Bo, Y. Duan, S. Shao and M. Tao, "Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder," in IEEE Transactions on Communications, vol. 72, no. 9, pp. 5626-5640, Sept. 2024

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