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 up-to-date 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.

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