42 evolution "https:" "https:" "https:" "https:" "https:" "https:" "Computer Vision Center" research jobs 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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sets that are relevant to industry demands while working on research projects in SIT. The researcher will be part of the team of the MCCS NATURE Project ( https://www.nparks.gov.sg/Cuge/Programmes
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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
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geometries, reusing relevant assets from other work packages where appropriate. Design, train and evaluate computer-vision and multimodal perception models for ULD alignment verification, CLS lock-state
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, strength development, fibre–matrix interaction, and phase evolution, and correlate microstructural characteristics with macroscopic performance. Conduct comprehensive mechanical testing of concrete
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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