Multi-modal Sensing AI Research Intern

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).