73 processor "https:" "https:" "https:" "IFM" positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT) in Singapore
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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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vision challenges) Experience with version control (Git) and collaborative development practices Where to apply Website https://www.timeshighereducation.com/unijobs/listing/408353/research-engineer-c
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multimodal AI algorithms for fire, smoke, and hot-work detection by fusing optical, thermal/infrared, LiDAR, RADAR, and gas sensor data under varying environmental conditions. Design computer vision and human
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Requirements Have relevant competence in the areas of Cyber Security. Degree in Infocomm, Computer Science, Cyber Security, Computer/Electrical Engineering, Information Technology or equivalent. Possessing a
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mechanisms) to ensure stable operation and precise control during flight. Edge Computing Implementation: Architect and deploy machine learning and computer vision models directly onto onboard edge devices (e.g
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Fellow or Research Engineer with strong expertise in Human-Computer Interaction (HCI) and social science methodologies to lead the user research and policy development, as part of an interdisciplinary team
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/training. Experience developing optimized modules in C#/C++ within Unity and/or Unreal Engine Experience with database management systems For network engineer role: Experience in computer networking
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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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(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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computer vision techniques, transformer architectures, and multi-modal learning. Familiarity with reinforcement learning (RL) principles, curriculum learning strategies, and the challenges of real-time