Master student (d/f/m) in the field of onboard AI/ML for 5G/6G Satellite Non-Terrestrial Networks
Core
Deploying hybrid CNN/LSTM AI models onto space-grade FPGA-based System-on-Chip (SoC) processors for 5G/6G satellite non-terrestrial networks.
Role type
Master thesis student (AI/ML for embedded space systems)
Builds
Next-generation satellite communication payloads
Domain
Aerospace / Satellite Communications / Embedded AI
Deliverable
production ML models
Required skills
Deep learning frameworks (PyTorch, TensorFlow), Python, C/C++, FPGA tools (AMD Xilinx Vitis/Vivado), model quantization, hardware partitioning, system profiling
Preferred skills
Satellite communications knowledge, 5G/6G networks, high-performance C/C++ kernel development
Technologies
FPGA, SoC, PyTorch, TensorFlow, Python, C/C++, AMD Xilinx Vitis/Vivado
Responsibilities
Analyze and partition AI models for workload distribution between neural processors and custom hardware; Optimize and quantize networks to reduce memory and power; Profile system performance for latency, throughput, and power efficiency; Validate model accuracy against 5G/6G standards.
Seniority
Student (Master thesis)
