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Intelligence (AI) services in Internet of Things (IoT) networks, where latency and timeliness are paramount. The successful candidate will be responsible for the end-to-end investigation of novel edge-assisted
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the Infocomm Technology cluster at SIT. This project addresses the critical challenge of delivering real-time Artificial Intelligence (AI) services in Internet of Things (IoT) networks, where latency and
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-cloud computation offloading algorithms. Set up network testbeds integrating hardware, software, and communication protocols to validate edge-only, cloud-only, and edge-cloud computation offloading
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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 and
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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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modelling, CFD mesh generation in complex 3D geometries. Proficient in handling large data sets and the ability to analysis and interpret results. Experience in commercial CFD package such as ANSYS package
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for a real-time AR/VR multiplayer system. (network engineer) Design, build, and implement the the game engine for both client and server. (game engine software engineer) Perform testing Perform
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programming will be advantageous. Knowledge of intelligent decision agents based on graph neural network or similar will an advantage. Key Competencies Good knowledge in reliability analysis. Experience in
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modelling, CFD mesh generation in complex 3D geometries. Proficient in handling large data sets and the ability to analysis and interpret results. Experience in commercial CFD package such as FDS and ANSYS
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decision agents based on graph neural network or similar will an advantage. Key Competencies Good knowledge in reliability analysis. Experience in FMECA and equipment health management will be advantageous