Machine Learning Engineer supporting the Enterprise Machine Learning team in developing advanced solutions. Collaborating with stakeholders and driving data science initiatives in a hybrid work environment.
FI
Hybrid Intern, Machine Learning-Based Channel Coding for Continuous-Valued Source Symbol Transmission
Posted 3 months ago
About the role
- Intern/Master Thesis in Machine Learning focusing on generative models for channel coding at Fraunhofer-Gesellschaft in Erlangen. Engage in research and practical applications in communication theory and signal processing.
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 and practical knowledge utilization.
- Opportunity to write a master's thesis in cooperation.
- Attractive opportunities to join the institute after studies.
Job title
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