AI Research Intern Wireless Foundation Models

At Bosch Research in Pittsburgh we are looking for an enthusiastic research intern to advance the state of the art in foundation models for wireless sensing and perception. The intern will explore how large-scale self-supervised and multimodal learning can enable generalizable representations of human activities and environments from radio-frequency (RF) signals and wireless channel measurements. 

The research will focus on developing foundation models that learn reusable representations from diverse wireless sensing data and transfer across sensing tasks environments devices and users. Potential applications include human presence detection activity recognition motion understanding and fine-grained behavioral and contextual understanding. 

The intern will work closely with Bosch researchers on the following responsibilities: 

• 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 environments users devices and downstream perception tasks. 

• Develop and evaluate methods for fine-tuning few-shot learning or zero-shot transfer to downstream wireless perception applications. 

• Conduct comprehensive experimental evaluations and comparisons with state-of-the-art approaches. 

• Develop a proof-of-concept demonstration of the proposed technology. 

• Summarize research findings and contribute to a high-quality research publication and/or patent application. 

Required 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 working with wireless sensing or RF signal data including channel measurements or related time-series signals. 

• Strong understanding of modern deep-learning architectures and training techniques. 

• 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. 

Qualifications :

• Experience with self-supervised learning foundation models transformers contrastive learning or generative models. 

• Experience with wireless sensing for applications such as presence detection activity recognition localization or smart-home sensing. 

• Experience collecting and processing wireless channel measurements complex I/Q samples signal-strength data or related RF sensing data. 

• Experience with RF data-acquisition pipelines wireless signal preprocessing or time-series feature extraction. 

• Experience with multimodal learning involving wireless signals language vision audio or other sensor modalities. 

• Experience with large-scale model training distributed training or GPU-based experimentation. 

• Strong publication record in leading machine learning sensing mobile computing or robotics venues such as ICLR NeurIPS ICML SenSys IPSN MobiSys MobiCom IMWUT/UbiComp or IROS. 

Additional Information :

All your information will be kept confidential according to EEO guidelines.

Indefinite U.S. work authorized individuals only.  Future sponsorship for work authorization unavailable.

Remote Work :

No

Employment Type :

Intern