Associate Director leading AI and data solutions for state and local government clients. Overseeing teams to drive value through advanced analytics, and AI - driven solutions.
FI
Hybrid Intern / Master Thesis – Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets
Posted last month
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.
Job title
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