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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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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . We are looking for a Research Fellow to lead advancements in multimodal large language models (LLMs) and embodied AI
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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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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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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 Key Responsibilities: The Research Fellow will
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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
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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Language Models (MLLMs) and their agentic implementations. Develop and benchmark novel adversarial attacks and defense strategies, focusing on the intersection of computer vision, natural language processing
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international law issues. Its mission is to enable Singapore and the Asia-Pacific region to play a more significant role in the promotion and development of international law. CIL's vision is to become a regional