Foundation Models for RF Sensing — Research Intern

Bosch; Pittsburgh (Allegheny County); On-site; USD 15,000 - 25,000; Full time

Job summary

Bosch Research in Pittsburgh seeks an enthusiastic research intern to advance foundation models for wireless sensing and perception using RF signals and channel measurements.

You will explore self-supervised and multimodal learning to obtain reusable representations across sensing tasks and environments, contributing to publications and potential patents while collaborating with Bosch researchers.

Qualifications

• 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.
• Experience with wireless sensing or RF signal data and time-series signals.
• Strong understanding of modern deep-learning architectures and training techniques.
• 2+ years Python programming with PyTorch.
• Ability to independently design, implement, and evaluate research ideas.
• Excellent written and verbal communication skills.

Responsibilities

• Develop foundation-model architectures and representation-learning methods for RF signals and wireless channel measurements.
• Investigate self-supervised, contrastive, generative, and multimodal learning for large-scale wireless sensing data.
• Study model generalization across environments, devices, and tasks.
• Develop methods for fine-tuning, few-shot, or zero-shot transfer to wireless perception tasks.
• Conduct experimental evaluations and compare with state-of-the-art approaches.
• Develop a proof-of-concept demonstration and contribute to publications or patents.

Skills

Python

PyTorch

Machine learning

Research

Communication

Education

PhD program in Computer Science, Electrical Engineering, Computer Engineering, Robotics, or related field

Tools

PyTorch

Job description

Bosch Research in Pittsburgh seeks an enthusiastic research intern to advance foundation models for wireless sensing and perception using RF signals and channel measurements.

You will explore self-supervised and multimodal learning to obtain reusable representations across sensing tasks and environments, contributing to publications and potential patents while collaborating with Bosch researchers.