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PhD Scholarship Develop AI and machine learning models to guide real-time, personalised treatment of paediatric brain cancer using multiomics and clinical data, within the internationally
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The successful PhD candidate will undertake research in the following areas: Develop deep learning algorithms for autonomous robotic navigation using dual-view image fusion and shadow-based visual perception
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PhD Scholarship in Multimodal Federated Learning and Medical Image Analysis Job No.: 695949 Location: Clayton campus Employment Type: Full-time Duration: 3-year and 3-month fixed-term appointment
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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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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hyper elastic materials 2. Design optimisation for programmed mechanical response Perform size, shape, or topology optimisation Combine with deep learning-based optimisation approach Additive
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surveys and fixed-camera monitoring of cliff faces, rock platforms and debris aprons. Apply change-detection tools (e.g., VoxFall) and deep-learning image segmentation to quantify debris dynamics and cliff
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Grant Arts PhD Fieldwork Grant Application is required. Check eligibility Key scholarship details Application status Open for applications Benefit amount Up to $12,000 Eligible study level Graduate