Multi-modal Sensing AI Research Intern

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At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people's lives. Our areas of activity are every bit as diverse as our outstanding Bosch teams around the world. Their creativity is the key to innovation through connected living, mobility, or industry.

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• Reinvent yourself: At Bosch, you will evolve.
• Discover new directions: At Bosch, you will find your place.
• Balance your life: At Bosch, your job matches your lifestyle.
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• Be yourself: At Bosch, we value values.
• Shape tomorrow: At Bosch, you change lives.

As the Multi-modal Sensing AI Research Intern, a few of your key responsibilities will include:

• Develop state-of-the-art multi-modal models for active (radar, ultrasound) and passive sensing (acoustic, vibration, EEG) use-cases using combination of classical signal processing and machine/deep learning-based approaches.
• Research and develop solutions for multi-modal representation learning and modality adaptation with paired / unpaired sensor data.
• Collaborate with other researchers to evaluate the developed model on downstream applications.
• Summarize research findings in high-quality paper and/or patent submissions.

Minimum Qualifications:

• Currently enrolled as PhD student in Computer Science, Electrical Engineering, or relatedfields.
• 2+ years programming experience, proficiency in PyTorch(Lightning), HuggingFace(transformers), hydra.
• Broad knowledge of machine- and deep-learning algorithms and principles and state-of-the-art methods.
• Minimum GPA of 3.0

Preferred Qualifications:

• Experience using raw sensor data (eg. Radar, ultrasound, acoustic etc.) in machine learning projects.
• Knowledge of digital signal processing principle and methods, multimodal representation learning.
• Publication record in top machine learning and signal processing venues.
• Experience with HPC platforms and job managers (Slurm, IBM LSF).

Equal Opportunity Employer, including disability / veterans