- Plan and execute cutting-edge research on developing novel machine learning models that can benefit public health.
- Conduct SOTA research while collaborating with other researchers, and engineers to develop, prototype, and test different AI/ML models at-scale for real-world health applications. Present and share results with key stakeholders.
- Troubleshoot, debug and upgrade existing models, contribute to code documentation and maintenance of the data pipeline.
- Currently has, or is in the process of obtaining, a PhD in computer science, electrical engineering, machine learning, biomedical engineering, or related fields.
- Experience with Python and in using deep learning libraries and related frameworks, such as Pytorch, TensorFlow, numpy, pandas, scikit-learn.
- Research skills involving defining problems, exploring solutions, and analyzing and presenting results.
- Deep understanding of at least one of the following areas: machine learning for biomedical engineering, natural language processing, computer vision, multimodal learning, self-supervised learning.
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
- Experience working and communicating cross functionally in a team environment
- Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as IWAENC, ICASSP, NeurIPS, or ICML.
- Proven track record in the design of novel and fieldable machine learning models for health related applications.
- Experience on working with time sequence form sensor data, such as PPG, CKG, IMU and audio.
- Intent to return to the degree-program after the completion of the internship/co-op.
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