
Post-Doctoral Researcher
- Canada
- $80,000 per year
- Permanent
- Full-time
- The development of probabilistic deep learning models that capture the temporal evolution of complex chronic diseases from sequential medical images (plus clinical information) to predict plausible outcomes for patients on and off treatments.
- Driving innovative research in causal representation learning, inference, and discovery; advance explainable models that enable discovery of image-based markers predictive of future disease evolution; and build fair, robust models for reliable predictions, along with uncertainty estimates.
- Advancing multimodal foundation models (images, text, clinical data), temporal 3D generative models for longitudinal MRI, and MLLMs/agentic-AI frameworks leveraging reinforcement learning for complex clinical-reasoning tasks.
- Collaborate with clinicians and researchers at the Montreal Neurological Institute and the Goodman Cancer Research Centre, with McGill and Mila teams, and with academic/industry partners (e.g. Stanford, Oxford, Google Research, Meta)
- Mentor and supervise graduate students.
- PhD in machine learning, with experience in applications in computer vision or medical image analysis.
- Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA, NeurIPS, ICML).
- Strong mathematical skills; programming skills and ML/DL experience (e.g., PyTorch/TensorFlow).
- Experience with temporal/longitudinal MRI and temporal 3D generative models.
- Background in uncertainty, explainability, fairness, and robustness for trustworthy predictions.
- Familiarity with multimodal foundation models, vision-language / MLLMs, agentic-AI, and reinforcement learning for clinical reasoning.