AI Research Intern, Wireless Foundation Models

What you'll do

• Develop novel foundation-model architectures and representation-learning methods for RF signals and wireless channel measurements.
• Investigate self-supervised, contrastive, generative, and multimodal learning approaches for large-scale wireless sensing data.
• Study model generalization and adaptation across different environments, users, devices, and downstream perception tasks.
• Develop and evaluate methods for fine-tuning, few-shot learning, or zero-shot transfer to wireless perception applications.
• Conduct comprehensive experimental evaluations and comparisons against state-of-the-art approaches.
• Develop a proof-of-concept demonstration of the proposed technology.
• Summarize research findings to contribute to high-quality research publications and patent applications.

What we're looking for

• Must be currently enrolled in a PhD program in Computer Science, Electrical Engineering, Computer Engineering, Robotics, or a related field.
• Must have a strong background in machine learning, deep learning, or representation learning.
• Must have experience working with wireless sensing or RF signal data, including channel measurements or related time-series signals.
• Must have a strong understanding of modern deep-learning architectures and training techniques.
• Must have 2+ years of programming experience in Python and strong proficiency with PyTorch.
• Must have the ability to independently design, implement, and evaluate research ideas.
• Must have excellent written and verbal communication skills.
• Must be an indefinite U.S. work authorized individual.