Student Assistant focusing on applying AI and Machine Learning techniques for research in an innovative team at Fraunhofer society.
FI
Hybrid Intern – Machine Learning-Based Channel Coding for Continuous-Valued Source Symbol Transmission
Posted 4 days ago
About the role
- Intern/Master Thesis focused on machine learning-based channel coding for continuous-valued source transmission at Fraunhofer Institute. Engaging in research and implementing innovative communication solutions.
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
- 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 (PyTorch or TensorFlow, NumPy, SciPy).
Benefits
- Flexible working hours that are perfectly compatible with your studies.
- Open and friendly working atmosphere where your ideas are valued.
- Variety of tasks that inspire and challenge you.
- Opportunities to join the institute on a full-time or part-time basis after your studies.
- Opportunity to write a master's thesis in cooperation with the institute.
Job title
Job type
Experience level
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Not specified
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