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their performance evaluated in terms of classification accuracy, computational speed, and overall usability. Required knowledge Deep learning (CNNs, Transformers) and computer vision Knowledge distillation for model
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research at leading international conferences. Indicative total stipend: approximately $54,280+ per annum (tax-free), plus up to $13,265 in travel support and access toEmotiv neurotechnology, computing and
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should ideally have experience in: Essential Deep learning and machine learning Computer vision Python programming PyTorch or TensorFlow Strong mathematical and analytical skills Desirable Video
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Image processing and computer vision Experimental data analysis and uncertainty quantification Piezoelectric actuation, acoustic systems or electronic driver development Eligibility and Project
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program should align with and contribute to the School’s Strategic Priorities and the University’s long term vision as outlined in the Impact 2030 statement. The Lecturer will lead and participate in
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, computer vision, federated learning, foundation models, adaptation techniques, multimodal learning, longitudinal image analysis or related areas, evidenced through coursework, research projects
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In EdgeFusionAI (EFAI): Real-Time Multi-Sensor Multi-Modal Intelligence on Edge Devices, we aim to design and develop efficient techniques for fusing heterogeneous sensory data, including vision
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privacy risks of AI systems used in healthcare, especially systems based on large language models, vision-language models, transformer models, and autonomous AI agents. The project may investigate attacks
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images