ResearchAssociate(ComputerScience/ComputerEngineering/ElectronicsEngineering/AI/MachineLearning)

Young and research-intensive, Nanyang Technological University, Singapore (NTU Singapore) is ranked among the world’s top universities. NTU’s College of Computing and Data Science (CCDS) is a leading college that is known for its excellent curriculum, outstanding and impactful research, and world-renowned faculty. A hot bed of cutting-edge technology and groundbreaking research, the College aims to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow. We welcome you to join our community of faculty, students and alumni who are shaping the future of AI, Data Science and Computing. **Key Responsibilities:** * Conduct research on 3D/4D scene reconstruction and rendering (e.g., NeRF, 3D Gaussian Splatting, novel view synthesis) and on scene understanding and interaction. * Develop generative AI for video and 3D/4D generation, world models, and vision-language models (VLMs) / vision-language-action (VLA) systems, with supporting large-scale data pipelines. * Publish in top-tier venues, contribute to open-source releases, mentor junior researchers, and report project progress. **Job Requirements:** * Master’s degree in Computer Science, Computer Engineering, Electronics Engineering, AI, Machine Learning or equivalent (PhD preferred). * Strong research record in computer vision / machine learning, with solid programming skills (e.g., Python, PyTorch) and demonstrated ability to conduct innovative research. * Independent, highly analytical, proactive and a team player * Excellent teamwork and verbal, written communication skills * In-depth knowledge relevant to the project domain. * Demonstrated capability to conduct innovative research. * The ability and willingness to adapt and incorporate novel ideas and approaches in scientific industry. * Demonstrated ability to formulate hypothesis and design effective experimental p