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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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strengths and contribute actively to their goals, including leading research programs that exploit molecular imaging capabilities to enhance the radiopharmaceutical program at UQ. These activities should
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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
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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(formerly the Australian Technology Park). Your key responsibilities will be to: Undertake original research in developing methods of cone beam computed tomography interventional imaging undertake
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imaging research. The role will focus on implementing infrastructure for scientific data movement and analysis workflows in AIS. The Research DevOps Engineer will be based in the Research Technology team
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. This would provide thousands of diverse example images with corresponding body part locations. These data would be used to train a deep learning model 5, 7 . The model’s high-quality body part predictions may
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of IT specialists | Hybrid working arrangement The Role We are seeking an enthusiastic individual with a passion for process excellence and improvement. This is a rare opportunity to make an impact as a
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qualifications in digital technologies, engineering, or equivalent experience; certifications (in Cloud Native Computing Foundation, Linux Foundation) highly regarded Collaboration & Communication: Strong ability
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intelligence, machine learning, automation, robotics, natural language processing, quantum computing, DevOps uplift and next-generation digital platforms. Enterprise Architecture is an equally critical component