Research Fellow (Artificial Intelligence / Machine Learning …, Singapore

School of Electrical and Electronic Engineering is one of the founding Schools of the Nanyang Technological University. Built on a culture of excellence, the School is renowned for its high academic standards and research. With over 3,000 undergraduates students and 2,000 graduate students it is one of the largest EEE schools in the world and ranks 4th in the field of Electrical & Electronic Engineering in the 2025 QS World University Rankings by Subjects.

Today, the School has become one of the world’s largest engineering schools that nurtures competent engineers and researchers. Each year, the School graduates over a thousand students who are ready to take on great ambitions and challenges.

We are seeking a Research Fellow to develop accelerated AI, machine learning, and robotics algorithms with a strong focus on computational efficiency, memory reduction, and energy‑aware deployment. The role targets foundation models, including large language models (LLMs), vision‑language models (VLMs), and vision‑language‑action models (VLAs), across a range of model scales and application scenarios. The Research Fellow will advance efficient AI techniques that enable scalable deployment on cloud, edge, and robotic platforms. The Research Fellow will contribute to the University’s mission by conducting high‑impact research, developing innovative efficiency‑oriented methods, and disseminating research outcomes through publications, collaborations, and open‑source contributions.

• Develop accelerated AI/ML and robotics algorithms that significantly reduce computation cost, memory footprint, and power consumption.
• Design and optimize efficient training and inference pipelines for foundation models (LLM, VLM, VLA) across different model sizes and deployment settings.
• Apply and advance model compression techniques, including quantization, pruning, knowledge distillation, low‑rank adaptation, and related methods.
• Conduct algorithm–hardware co‑design to enable efficient and accurate deployment of AI algorithms on robotic, edge, and heterogeneous computing platforms.
• Develop methods for efficient deployment of AI models on cloud and edge devices, considering latency, throughput, and energy constraints.
• Implement, evaluate, and benchmark accelerated models using rigorous experimental protocols.
• Contribute to research publications in top‑tier AI, ML, and robotics conferences and journals.
• Supervise and mentor PhD students, junior Research Assistants, and Master students.
• Collaborate with interdisciplinary teams spanning AI, systems