AI Research Intern: Wireless Foundation Models

What you'll do

• Develop and evaluate foundation-model architectures and learning methods for wireless sensing, including self-supervised and multimodal approaches.
• Study generalization and transfer across environments, users, devices, and downstream tasks; build a proof-of-concept demonstration.
• Evaluate methods, report findings, and contribute to a research publication and/or patent application.

What they ask for

• Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Computer Engineering, Robotics, or a related field.
• Strong background in machine learning, deep learning, or representation learning, and experience with wireless sensing or RF signal data.
• 2+ years of programming experience in Python and strong proficiency with PyTorch.
• Ability to independently design, implement, and evaluate research ideas; excellent written and verbal communication skills.

Nice to have

• Experience with self-supervised learning, foundation models, transformers, contrastive learning, or generative models.
• Experience with multimodal learning, RF data acquisition or processing, large-scale or distributed training, or publications in relevant venues.

Tools

Python, PyTorch, RF signals, wireless channel measurements, transformers

AI focus

Research and develop self-supervised, contrastive, generative, and multimodal foundation models for wireless sensing and perception, including transfer and adaptation across tasks and settings.