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 streamsTrain multimodal architectures mapping data into unified latent spacesDistinguish cognitive variation from measurement and language confoundsDesign evaluations for robustness,



separation, and generalizationScrutinize bias, sensitive inferences, stability, and responsible data usageRequirements:PhD in machine learning, computer science, or related disciplineProficiency in Python and up-to-date deep learning frameworksExperience with high-performance computing infrastructure and GPU clustersResearch background in multimodal modeling and representation learningExperience with statistical machine learning model evaluationApply multimodal modeling, representation learning, and rigorous evaluation to neurotype research at McGill University in Montreal.

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