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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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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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advance research in computer vision, machine learning, and/or robotics for the digitalization, monitoring, and automation of civil infrastructure. The role will focus on developing innovative methodologies
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Science, Artificial Intelligence, Computer Vision, or a related field. Proficient in gait analysis and human data mining, capable of representing, extracting, and interpreting complex data sets for disease prediction
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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computer vision and visual generation. To produce research reports and/or publications as required by the funding body or for dissemination to the wider academic community. To provide guidance and support to
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given - Perform microscopy and nanodevice analysis Program and design PLC systems Apply Computer Vision for machine hardware feedback and control Generate technical reports, white papers, and
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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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ensure high-quality delivery. Job Requirements Have relevant competence in the areas of computer vision. Have a Bachelor’s or Master’s degree in computer science, data science, AI, or related fields
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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