Multi-modal Sensing AI Research Intern

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

Responsibilities

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.

Requirements

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

Nice to have

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).