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 modern 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 in Montréal employer: McGill University
McGill University is an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration in the heart of Montreal. As an Assistant Professor, you will have access to extensive resources for research and teaching, along with opportunities for professional development and community engagement, making it a rewarding environment for academic growth and impactful contributions.
