• Designing novel AI architectures and training algorithms to improve the efficiency, robustness, and scalability of generative models across multi-modal tasks.
• Creating and testing single- and multi-agent systems, introducing new optimization and learning strategies that redefine state-of-the-art performance.
• Investigating innovative techniques in model alignment, adaptive learning, and resource-efficient training for real-world impact.
• Advancing foundation models for urgent global sustainability challenges.

You will help define research challenges, prototype and evaluate solutions, design experiments, and share findings through patent applications and publications in top-tier venues.

To Apply Please Submit

• CV
• One-page cover letter highlighting relevant experience, especially in AI, algorithmic innovation and the research themes described above.

Preferred Education

Master's Degree

Required Technical And Professional Expertise

• Enrollment in a master’s or PhD program in computer science, mathematics, statistics, or a related field.
• Strong programming skills (e.g., Python).
• Demonstrated ability to tackle analytical problems with rigorous, quantitative approaches.
• Experience developing, training, and evaluating deep neural networks.

Preferred Technical And Professional Experience

• Expertise with ML frameworks such as PyTorch.
• Research or practical experience with generative models (e.g., large language models, multi-modal foundation models).
• Background in algorithm design or optimization techniques (e.g., novel training objectives, efficient inference strategies).
• Record of clear research communication, evidenced by publications or presentations at premier AI conferences (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, CVPR).