Develop multimodal machine learning methods at McGill University in Montreal, integrating cognitive, conversational, and biometric data through self-supervised learning.

As a Postdoctoral Fellow, you will build a foundation model of human neurotypes from diverse individual-difference data. Your research will combine tokenization, multimodal architectures, latent-space modeling, and psychometric analysis. The work includes evaluating robustness, missing modalities, participant-level separation, and generalization while examining bias and sensitive inferences.

Key Responsibilities:

- Develop tokenization and self-supervised methods for diverse data streams
- Train multimodal architectures mapping data into unified latent spaces
- Distinguish cognitive variation from measurement and language confounds
- Design evaluations for robustness,



separation, and generalization
- Scrutinize bias, sensitive inferences, stability, and responsible data usage

Requirements:

- PhD in machine learning, computer science, or related discipline
- Proficiency in Python and contemporary deep learning frameworks
- Experience with high-performance computing infrastructure and GPU clusters
- Research background in multimodal modeling and representation learning
- Experience with statistical machine learning model evaluation

Apply multimodal modeling, representation learning, and rigorous evaluation to neurotype research at McGill University in Montreal.

📌 Postdoctoral Fellow Multimodal Machine Learning (Montreal)
🏢 McGill University
📍 Montreal