Research Fellow/Engineer (AI On Video Analytics) - Dw(1074)

About the Project The SITx NVIDIA AI Centre (SNAIC) at the Singapore Institute of Technology(SIT) is seeking a highly motivated Research Fellow/Engineer to join a strategic collaboration with Procter & Gamble (P&G) . This project aims to develop next-generation AI technologies for concept-driven video understanding from consumer-recorded facial care videos. Unlike conventional action recognition, the research focuses on understanding high-level semantic concepts, behavioural patterns, product usage, and skin care routines through advanced Vision AI, Multimodal AI, and Foundation Models. The successful candidate will work closely with researchers from SNAIC , NVIDIA , and P&G to develop novel AI methodologies with direct industrial impact. Responsibilities The successful candidate will: Conduct cutting-edge research in Computer Vision, Video Understanding, and Multimodal AI. Design AI models for concept-driven video understanding of consumer facial care behaviours. Work with PI and company to develop the AI solution Develop novel algorithms for: - Fine-grained video understanding - Concept learning - Temporal reasoning - Vision-Language Models (VLMs) - Multimodal representation learning - Self-supervised and weakly supervised learning Fine-tune and adapt Foundation Models and Vision-Language Models for domain-specific applications. Build end-to-end AI pipelines for video analytics and deployment. Work with industrial datasets and collaborate with domain experts from P&G. Publish research findings in top-tier AI conferences and journals. Mentor graduate students and support research activities within SNAIC. Required Qualifications Applicants should possess: PhD/Ms/BSc in Computer Science, Artificial Intelligence, Electrical Engineering, or a related discipline. Strong research background in one or more of: - Computer Vision - Machine Learning - Deep Learning - Video Understanding - Multimodal AI Excellent programming skills in Python. Hands-on experience with: - PyTorch - Hugging Face Transformers - OpenCV Experience working with deep learning models for image or video analysis.