AI Prompt Engineer focusing on developing conversational AI experiences for healthcare professionals at Elsevier. Join a team creating innovative solutions powered by generative AI.
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Hybrid Intern – Master's Thesis, Generative AI for Joint Source and Channel Coding of Short Multimedia Packets
Posted last month
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.
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Degree requirement
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