Hybrid Intern / Master Thesis – Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets

at Fraunhofer IIS

• Job title: Intern / Master Thesis – Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets
• Job type: Internship
• Experience level: Entry level
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Numpy, Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Erlangen, Germany

About the role

• Intern working on a Master's thesis focusing on generative AI for physical layer communication. Collaborating with R&D teams at Fraunhofer Institute in Erlangen, Germany.

Responsibilities

• Conduct a comprehensive literature review on generative models applied to the physical layer of communication.
• Design and implement generative AI–based transmission schemes (e.g., using VAE, GAN, or diffusion models).
• Evaluate the performance of these schemes against conventional digital baselines in terms of distortion, reliability, and efficiency.

Requirements

• You are studying in the fields of communication theory, signal processing, or machine learning.
• You have a solid understanding of physical-layer concepts, including modulation and channel coding.
• You have hands-on experience with Python and machine learning frameworks such as PyTorch or TensorFlow, as well as NumPy and SciPy.

Benefits

• Flexible working hours that fit well with your studies.
• An open and friendly working atmosphere where your ideas are valued.
• A variety of tasks that are both inspiring and challenging.
• Opportunity to participate in application-oriented research and apply your theoretical knowledge in practice.
• Exciting, pioneering projects that make a meaningful impact.