Fraunhofer IIS

Intern, Machine Learning-Based Channel Coding for Continuous-Valued Source Symbol Transmission

Fraunhofer IIS

internship

Posted on:

Location Type: Hybrid

Location: Erlangen • Germany

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Job Level

Entry Level

Tech Stack

NumpyPythonPyTorchTensorflow

About the role

  • 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 and practical knowledge utilization.
  • Opportunity to write a master's thesis in cooperation.
  • Attractive opportunities to join the institute after studies.
Applicant Tracking System Keywords

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

Hard Skills & Tools
generative modelstransmission schemesVAEGANdiffusion modelsperformance evaluationmodulationchannel codingPythonmachine learning