Hybrid Intern – Master's Thesis, Generative AI for Joint Source and Channel Coding of Short Multimedia Packets

Posted last week

Apply now

About the role

  • Intern for Master Thesis on Gen-AI for Joint Source and Channel Coding of Short Multimedia Packets at the Fraunhofer Institute. Engaging in innovative research with flexible hours and collaboration opportunities.

Responsibilities

  • Conduct a comprehensive literature review on the application of generative models to the physical layer of communication systems.
  • 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 with respect to distortion, reliability, and efficiency.

Requirements

  • Enrolled in a degree program in communication theory, signal processing, electrical engineering, computer science, or a related field.
  • Solid understanding of physical-layer concepts, including modulation and channel coding.
  • Practical experience with Python and machine learning frameworks such as PyTorch or TensorFlow, and proficiency with NumPy and SciPy.

Benefits

  • Flexible working hours that fit well with your studies.
  • An open and friendly work environment where your ideas are valued.
  • Diverse and stimulating tasks that challenge and inspire you.
  • Application-oriented research opportunities to apply theoretical knowledge.
  • Exciting, pioneering projects that have real-world impact.

Job title

Intern – Master's Thesis, Generative 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

Report this job

See something inaccurate? Let us know and we'll update the listing.

Report job