Technical Research Assistant

1 week ago

Toronto, Canada M31 AI Full time

**Overview**

Read the entire description before applying. **MUST HAVE**:Familiarity with toolkits such as MONAI, nnUNet, ITK-SNAP, MedSAM, 3D Slicer

This is an ideal opportunity for graduate students, recent grads, or students completing a practicum, thesis, or capstone project, looking to gain hands-on experience in applied machine learning in healthcare. You’ll work with large, real-world imaging datasets and contribute directly to building and evaluating deep learning models with real clinical impact.

**What You’ll Do**
- Preprocess and clean multi-institutional MRI, CT, and angiography datasets (de-identification, normalization, reformatting)
- Generate segmentation masks in collaboration with radiologists and clinical domain experts using tools like MedSAM and ITK-SNAP
- Implement and evaluate classification models using MONAI to predict clinical outcomes (e.g., recurrence risk)
- Build end-to-end segmentation pipelines with nnUNet for identifying key regions of interest
- Conduct model validation (internal, cross-site, and external testing) using standard metrics like AUROC, Dice, NSD, sensitivity/specificity
- Maintain reproducible workflows and clean, well-documented codebases using Git, Jupyter, and UNIX-based tools
- Collaborate closely with AI scientists, clinicians, and imaging experts across a high-performing interdisciplinary team

**Why Join Us**
- Gain hands-on experience with one of the most promising areas of healthcare AI: pediatric neuroimaging
- Eligible to be counted as practicum or experiential learning credit for graduate or professional programs (check with your program advisor)
- Learn to develop, deploy, and validate AI models in a healthcare research environment
- Receive mentorship from clinicians, AI researchers, and imaging scientists
- Contribute to peer-reviewed publications and high-impact research
- Work with large-scale imaging data from leading hospitals and research centers

**Required Skills & Background**
- Bachelor’s degree in Computer Science, Biomedical Engineering, Physics, or related field (Graduate students encouraged to apply)
- Proficiency in Python and deep learning frameworks (especially PyTorch)
- Strong grasp of machine learning fundamentals, particularly in classification and segmentation
- Experience working with 2D or 3D imaging data (MRI, CT, etc.)
- Familiarity with toolkits such as MONAI, nnUNet, ITK-SNAP, MedSAM, 3D Slicer
- Understanding of common model evaluation metrics (e.g., AUROC, Dice, NSD)
- Experience using Git, UNIX systems, and Jupyter notebooks
- Strong analytical thinking, independence, and attention to reproducibility

**Nice-to-Have**
- Prior experience working with medical or biological datasets
- Experience generating segmentation masks or annotating imaging data
- Familiarity with pediatric or neurological imaging datasets

**Application Requirements**
- Resume/CV
- Brief cover letter outlining technical experience and interest in the position
- Unofficial transcript
- GitHub portfolio, code samples, or publications (optional but encouraged)

About Us

M31 is a multidisciplinary AI and health research team working with Canada’s top hospitals and academic centers, working on projects that bridge cutting-edge machine learning with urgent clinical needs.

**Job Types**: Full-time, Internship / Co-op
Contract length: 12 months

Pay: $35.00-$45.00 per hour

Expected hours: 40 per week

**Benefits**:

- Flexible schedule
- Work from home

Work Location: Hybrid remote in Toronto, ON M5S 1A8

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