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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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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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will play a key role in automated wildlife identification and classification from trap camera images using cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI
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cutting-edge computer vision technology. Working closely with the Principal Investigator, Co-PI, and interdisciplinary research team, RE will develop and implement deep learning algorithms to analyze trap
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About RoboPrecinct@PDD SmartPrecinct@PDD initiative aims to develop a precinct-scale robotics and embodied AI ecosystem at Punggol Digital District (PDD). The programme establishes shared digital
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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, design, testing, performance optimisation, databases, computer graphics, distributed systems, computer vision, and multimedia analytics. AI and Emerging Fields: Industrial IoT, computational fintech, robot
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candidates to join us in realising our vision. We offer competitive renumeration, generous employment benefits, access to funding to undertake research of relevance to industry, and opportunities to inspire
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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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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