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. • be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. Selection criteria Skillset: Proficient in Python, machine learning, and
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PhD Scholarship Develop multimodal machine learning models to predict glioblastoma treatment outcomes using imaging and clinical data. Work with real-world data from John Hunter Hospital in a
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning
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This PhD research scholarship (Learning Lessons from Drug Resistance to Tackle Herbicide Resistance) is funded by the Australian Research Council to support a full-time PhD student to undertake
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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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learning and modelling to reveal plant-soil-microbe interactions. Your findings will inform climate and environmental policy and may contribute to shaping Australia’s carbon credit system. You’ll work with a
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for alternative analytical approaches for vaccine–antigen quantification. You will then learn current industry best practice methods involving ELISA assays and biosensor technology at Zoetis before developing and
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accelerate the global shift to electric mobility. This PhD will build an AI-driven fleet-scheduling framework that learns from battery data in real time and optimises charging, operations and maintenance