Support the Data & AI Products team in developing AI - driven market research applications. Gain hands - on experience with LLMs, RAG, prompt engineering and modern web technologies.
FI
Hybrid Intern, Master's Thesis – Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets
Posted last week
About the role
- Intern focusing on generative AI for multimedia packet coding at the Fraunhofer Institute in Erlangen. Engaging in cutting-edge research at the intersection of machine learning and communication engineering.
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.
- Solid understanding of physical layer concepts, including modulation and channel coding.
- Hands-on experience with Python and machine learning frameworks such as PyTorch or TensorFlow, and with numerical libraries like NumPy and SciPy.
Benefits
- Flexible working hours that are fully compatible with your studies.
- Open and friendly working atmosphere where your ideas are valued.
- Diverse tasks that inspire and challenge you.
- Application-oriented research and opportunities for practical experience.
- Attractive possibilities to join the institute on a full-time or part-time basis after graduation.
- Opportunity to write a master's thesis in cooperation with the institute.
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