Postdoctoral Associate - Efficient and Distributed Machine Learning

About the role

We invite applications for a Postdoctoral Associate position in the broad areas of efficient machine learning and distributed learning. The successful candidate will contribute to advancing both the theoretical foundations and practical implementations of scalable AI systems.

• Efficient training and deployment of machine learning models, including optimization, quantization, pruning, and model compression.
• Distributed, collaborative, and federated learning, with attention to robustness, communication efficiency, and personalization.
• Theoretical analysis of convergence, generalization, and trade‑offs in scalable or decentralized learning algorithms.

This is an excellent opportunity to work at the intersection of mathematical rigor and practical relevance, in a dynamic and collaborative research setting.

Key Responsibilities

• Design and implement novel algorithms for scalable, efficient...