Postdoctoral Associate - Efficient and Distributed Machine Learning

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is the world’s first AI-focused university. Founded in 2019, MBZUAI is a research-oriented graduate-level university that has grown to host over 80 world-class faculty, over 330 graduate-level students, and is already ranked in the world’s top 25 by CSRankings for AI-related fields. Located in Abu Dhabi, the university aims to become a global leader in AI education and research by providing cutting-edge programs in Artificial Intelligence, empowering students to shape the future of technology. MBZUAI is committed to fostering innovation, collaboration, and ethical practices in AI to address global challenges.

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.

The research will focus on (but is not limited to):

• 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, and decentralized training of ML models.
• Conduct theoretical research on optimization, generalization, or communication-constrained learning.
• Translate theoretical insights into practical methods deployable in real-world settings.
• Publish in top-tier conferences and journals (e.g., NeurIPS, ICML, ICLR, COLT, JMLR).
• Contribute to open-source software and reproducible research pipelines.
• Mentor graduate students and collaborate across interdisciplinary teams.

Qualifications

A Ph.D. in Computer Science, Electrical Engineering, Applied Mathematics, or a related field.

Minimum:

• A strong publication record in machine learning, optimization, or distributed systems.
• Proficiency in deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Solid programming skills and analytical thinking.
• Effective written and verbal communication.

Preferred:

• Demonstrated ability to bridge theory and practice in scalable ML.
• Experience with federated learning, quantization/pruning, or systems-level ML design.
• Familiarity with topics such as stochastic optimization, communication-efficient algorithms, or model compression.
• Previous mentorship experience or collaborative research across domains.

What we offer:

• Significant opportunities for professional development and travel to major conferences.
• Competitive compensation and benefits package aligned with top-tier academic markets.
• A vibrant, interdisciplinary research environment at a globally recognized AI university.
• Access to state-of-the-art HPC infrastructure.
• A one-year appointment with the possibility of renewal based on performance and project alignment.

Application Instructions

Interested candidates should submit the following documents:

1. Cover Letter
2. Curriculum Vitae (C.V.)
3. A link to your Google Scholar profile.
4. A 1–2 page research statement outlining directions you would like to pursue in efficient or distributed machine learning. (Optional)

Applications will be reviewed on a rolling basis, and the position will remain open until filled.

For more information or to apply, please visit MBZUAI Careers.