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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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degree will definitely be advantageous. Knowledge of machine learning, pytorch, huggingface etc... Knowledge of image processing is required. Ability to effectively and efficiently utilise industry
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in academic writing Possess good critical thinking skills Show strong initiative and take ownership of work Interest in AI, machine learning, image/audio processing
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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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vision and deep learning concepts, including object detection and image-based classification, with hands-on experience using Python and at least one deep learning framework (e.g., PyTorch or TensorFlow
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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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, including design approaches, scanning methods, signal processing techniques, and comparison with alternative detection technologies. ii. Support design and development of NQR prototype, including system
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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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of the award, awardees will be considered for Assistant Professorship. Awardees will also be assigned a faculty mentor for the duration of the scheme. Application Process Applications are open throughout
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foundational knowledge in signal processing and machine learning. Working knowledge of computer vision and deep learning concepts, including object detection and image-based classification, with hands