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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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the R&D Team Knowledge in skid module base and PLCs programming will be highly preferred Possess good Solid-works skillset and/or ASPEN HYSYS/3D simulations expertise Sound knowledge in applying AI
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various engineering related presentations, both internally and externally Support the R&D Team Knowledge in skid module base and PLCs programming will be highly preferred Possess good Solid-works skillset
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of high-efficiency, compact, and reliable solutions. Candidates with relevant R&D work experience will be considered. The project supervisor will provide on-the-job training on simulation tools. Key
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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an advantage. Ability to use statistical packages such as SPSS, R for statistical analyses and visualisation will be an advantage. Some research and/or clinical experience/interest in the area of stroke
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques