We're seeking a graduate student or postdoc in machine learning who’s eager to gain hands-on experience at a cutting-edge healthtech startup. You'll collaborate closely with leading professors, experienced scientists and developers, and successful entrepreneurs. An immediate start is preferred, with full-time availability through the summer. Part-time or flexible arrangements are also welcome.
Responsibility
- Design, build, and debug new neural network architectures from scratch
- Adapt and optimize existing deep learning models for challenging data cases
- Diagnose and interpret model limitations using performance metrics
- Work with regression and classification models, including waveform/time-series signals and imbalanced datasets
- Implement strategies for individual frame-to-patient data aggregation
- Contribute to end-to-end ML pipelines, including training and evaluation
- Apply methods such as multi-instance learning, self-supervised learning, or contrastive learning
Requirements
- Experience in coding, ideally Python and MATLAB
Valuable To Have
- Experience with ultrasound data or related domains (e.g., audio, non-destructive testing, geophysics)
- Ideally based in Toronto for Hybrid work at our Toronto Office, 155 Queens Quay East
- Familiarity with clinical data workflows or biomedical applications
- Motivated, driven, collaborative, and eager to learn and produce real-world results