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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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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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. • 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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of system and data confidentiality and complete any other requirements. Desirable skills: Experience in programming (C, python, or similar). Knowledge in machine learning or distributed systems. How to apply
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components. The resulting data will be used to train a machine learning (ML) model, enabling automated and efficient beamline alignment. This technology has the potential to significantly enhance
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This Australian Research Council (ARC) Discovery PhD project addresses Australia’s urgent challenge of restoring soil carbon in semi-arid ecosystems, which cover 80% of the continent and have lost
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catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon our groundbreaking research into the problem. Required knowledge A solid grounding in machine learning
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to simulate key optical features and explore optimal alignment of beamline components. The resulting data will be used to train a machine learning (ML) model, enabling automated and efficient beamline alignment