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

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About the role

  • Intern for master thesis focusing on generative AI for communication systems at Fraunhofer Institute. Engaging in literature review, design, and performance evaluation of AI-based transmission schemes.

Responsibilities

  • Conduct a comprehensive literature review on generative models applied to physical layer 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 study in the field of communication theory, signal processing, and 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, NumPy, SciPy.

Benefits

  • Flexible working hours that are perfectly compatible with your studies.
  • Open and friendly working atmosphere in which your ideas are valued.
  • Diverse tasks that inspire and challenge you.
  • Application-oriented research to put your theoretical knowledge to practice.
  • Exciting and pioneering projects that make a real difference.
  • Opportunity to write a master's thesis in cooperation with the institute.

Job title

Intern – Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets

Job type

Experience level

Entry level

Salary

Not specified

Degree requirement

No Education Requirement

Location requirements

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