Fraunhofer IIS

Intern, Master's Thesis – Machine Learning-Based Channel Coding for Continuous-Valued Source Symbol Transmission

Fraunhofer IIS

internship

Posted on:

Location Type: Hybrid

Location: Erlangen • Germany

Visit company website

Explore more

✨ AI Apply
Apply

Job Level

Entry Level

Tech Stack

NumpyPythonPyTorchTensorflow

About the role

  • 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 VAEs, GANs, 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.
  • Hands-on experience with Python and machine learning frameworks such as PyTorch or TensorFlow, and familiarity with NumPy and SciPy.
Benefits
  • Flexible working hours compatible with your studies.
  • Open and friendly working atmosphere where your ideas are valued.
  • Varied tasks that inspire and challenge you.
  • Exciting, pioneering projects that make a real impact.
  • Opportunities to join the institute on a full-time or part-time basis after your studies.
  • Flexible scheduling of working days within the fixed-term employment contract.
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
generative modelstransmission schemesvariational autoencodersgenerative adversarial networksdiffusion modelsperformance evaluationmodulationchannel codingPythonmachine learning