26 coding-"https:" "https:" "https:" "Computer Vision Center" Fellowship positions at Singapore Institute of Technology
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Job Description Apply Now Research Engineer/ Fellow (Applied AI research in computer vision, vision-language models) (XBJ) Posting Start Date: 27/02/2026 Schemes of Service: Research Division
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Job Description Apply Now Research Engineer/Fellow (Deep Learning Computer Vision - SHNeo) Posting Start Date: 25/06/2026 Schemes of Service: Research Division: Professional Officers Employment
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to ensure project deliverables are met. Any other ad-hoc duties as assigned by Supervisor. Requirements PhD in Computer Science or related field Expertise in computer vision and vision-language
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on: Signal processing combined with AI-based modification on speech signals. The need is for a real-time system, so efficient coding is important. Key Responsibilities: • Participate in and manage
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documentation required for project reviews, dissemination, and future translation/commercialization. Supervise and mentor junior research staff and students, including guidance on experiments, coding, data
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solutions for automated defect inspection and condition assessment of train components by designing and developing deep learning, computer vision, and machine learning algorithms. Collaborating with
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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 disciplines. Knowledge of
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, Deep Learning, Computer Vision, Large Language Models, Data Analytics, or Intelligent Systems will be advantageous. Key Competencies Prior experience in research project coordination and management
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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, BLIP) or scene-graph
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in computer vision techniques, transformer architectures, and multi-modal learning. Familiarity with reinforcement learning (RL) principles, curriculum learning strategies, and the challenges of real