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Machine learning has recently made significant progress for medical imaging applications including image segmentation, enhancement, and reconstruction. Funded as an Australian Research Council
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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: haoran.ren@monash.edu.
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This Masters or PhD project aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain ML predictions and
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they bond in materials, but also develop transferable skills in scientific computing, data analysis and visualisation. "Machine learning for atomic-scale structure determination in thick nanostructures" (with
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Environmental Engineering Mechanical and Aerospace Engineering Electrical and Computer Systems Engineering Chemical and Biological Engineering Materials Science and Engineering About the Role We are seeking
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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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experience with programming (e.g., Python), machine learning, or educational data is beneficial, it is not a strict requirement. The project provides ample opportunities to develop these skills over time. What
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this project, we will develop automated approach to detect the defects in AI systems, including LLMs, auto-driving systems, etc. Required knowledge - self-motivated, willing to spend time and efforts in research
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awardee. The Opportunity Generative artificial intelligence is a significant and highly visible use of machine learning which has become commonplace in a matter of a few short years. Without common
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the area of end-to-end modular autonomous driving using computer vison and deep learning methods. This includes developing an efficient and interpretable image processing, vision-based perception and