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What You’ll Be Doing:
Design and prototype deep learning models for wireless signal processing tasks such as channel estimation, beam alignment, link adaptation, and scheduling.
Work with simulation tools and real-world datasets to build models that generalize across diverse wireless scenarios.
Implement, train, and validate neural networks (e.g., CNNs, Transformers, GNNs) using PyTorch or TensorFlow.
Collaborate with researchers and system engineers to integrate models into full-stack RAN.
Optimize model performance for real-time inference and hardware acceleration.
Contribute to model evaluation, benchmarking, and deployment readiness on GPU platforms.
What We Need to See:
MS or PhD in Electrical Engineering, Computer Engineering, or a related field (or equivalent experience).
12+ years of experience in wireless communications, signal processing, or AI/ML.
Deep understanding of communication systems (e.g., MIMO, OFDM, fading channels) and DSP fundamentals.
Strong experience in training and deploying deep learning models for time-series or signal-based tasks.
Proficiency in Python and experience with DL frameworks like PyTorch or TensorFlow.
Familiarity with tools such as MATLAB, GNU Radio, or NVIDIA Sionna for wireless simulation.
Ways to Stand Out from the Crowd:
Experience with AI for 5G/6G systems, AI-for-RAN architecture, or telecom-grade deployments.
Knowledge of channel estimation by AI, model compression, real-time inference, or GPU optimization
Exposure to CUDA, Triton, or real-time inference pipelines.
Contributions to research publications or open-source wireless/AI projects.
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