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on computer vision. The role involves developing and advancing novel algorithms for emerging challenges in computer vision, including continual learning and few-shot learning. The candidate is also expected
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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advance research in computer vision, machine learning, and/or robotics for the digitalization, monitoring, and automation of civil infrastructure. The role will focus on developing innovative methodologies
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Disease Programme (SPARKLE) - Theme 5: Neurotech Intervention, we establish a sub-project on “Human Gait Detection via Acoustic-enabled Footstep Tracking under NTU-CCDS. In line with NTU’s vision and
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. This role will contribute to NTU’s mission of driving transformative research in artificial intelligence and robotics by developing force-integrated Vision-Language-Action models that enable seamless human
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computer vision and machine learning. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and support to
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Engineering, Mechatronics, Computer Science, etc. Strong background in AI, Vision Language Model, end-to-end autonomous driving, deep learning, computer vision, robotics and automation. Candidates having
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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for a Senior Research Fellow to help lead a vibrant, internationally connected research programme spanning Bayesian infectious disease modelling, AI-driven epidemic forecasting, genomic epidemiology, and
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computer vision techniques, transformer architectures, and multi-modal learning. Familiarity with reinforcement learning (RL) principles, curriculum learning strategies, and the challenges of real-time