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.

For more details, please view: https://www.ntu.edu.sg/eee

We are seeking a Research Associate 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 Associate will advance efficient AI techniques that enable scalable deployment on cloud, edge, and robotic platforms. The Research Associate 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.

Key Responsibilities:

• 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.
• Collaborate with interdisciplinary teams spanning AI, systems, and robotics.
• Contribute to open-source codebases and reproducible research practices.

Job Requirements:

• A Master's degree in Computer Science, Electrical Engineering, Robotics, Artificial Intelligence, or a closely related field.
• Strong research background in AI and machine learning, with a focus on efficient or accelerated models.
• Proven experience with model compression techniques, such as quantization, pruning, distillation, and low-rank adaptation.
• Demonstrated experience working with foundation models, particularly vision-language models (VLMs); experience with LLMs or VLAs is a strong advantage.
• Solid understanding of algorithm-hardware co-design, especially for robotics or edge AI deployment.
• Strong programming skills in C, C++, and Python, with experience in deep learning frameworks such as PyTorch or TensorFlow.
• Familiarity with deployment constraints on cloud, edge, or embedded systems.
• Experience in robotics, embodied AI, or autonomous systems is an advantage.
• Strong publication record or clear potential to publish in leading international venues.
• Ability to work independently, manage complex research tasks, and collaborate effectively in interdisciplinary teams.
• Good written and oral communication skills.

We regret to inform that only shortlisted candidates will be notified.