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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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on developing the imaging system as well as novel machine learning approaches for image analysis and disease classification using field data from German and Brazilian agricultural trials. Responsibilities Design
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programming languages such as Python and experience with deep learning frameworks (e.g., PyTorch, TensorFlow) is highly desirable strong interest in interdisciplinary research combining imaging, machine
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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Bachelors Honours degree (or equivalent) in an appropriate discipline. Ideal candidate will have some prior knowledge in deep learning and computer graphics. Subject Area Medical imaging, biomedical
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Computer Science or MSc in related field. Skills Knowledge of programming, Computer Vision, Machine Learning, or related discipline. Experience with Python and relevant AI/ML libraries (e.g., PyTorch, TensorFlow
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quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a welcoming and international work environment. The research group in
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improvements in machine learning (ML) applications now allow researchers without extensive programming backgrounds to implement advanced image-processing techniques using accessible programming languages and
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frameworks. This approach is timely, as improvements in machine learning (ML) applications now allow researchers without extensive programming backgrounds to implement advanced image-processing techniques
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protocols to characterize both cellular and vascular properties of the TME. The approach will be validated using a combination of in silico models, computer simulations, and in vitro experiments using tumor