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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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in POD, they jointly develop central policies and processes for the safe and seamless operation of laboratories in SIT. Key Responsibilities Design and teach labs & practice modules. Mentor students in
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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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moulds electronic and computer engineers, computer scientists, AI engineers, interactive media and game development experts, software engineers, and information security specialists. We invite applications
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available at all levels from Lecturer to Full Professor in Chemical Engineering or related disciplines. Priority skill areas include: process monitoring and control, industry 4.0, modeling, AI and ML
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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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to determine the success of the prototype, Propose and develop trials and experiments acceptance test steps/procedures, Process and analyse the data gathered from the trial and experiments. Prepare the final
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, minutes, and programme documentation. Maintain programme documentation and information repositories. Resource management Manage procurement processes for programme-related services, equipment, and
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partners; prepare collaboration contracts/agreements and support the onboarding process for companies joining the SmartPrecinct ecosystem. Support reporting and documentation of industry partnerships and
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. The role involves implementing retrieval-augmented natural language inference models and LLMs, processing large-scale financial text data, and supporting empirical analysis of contradiction scores. Job