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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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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 5 days ago
and orchestration technologies for real-world logistics and decision support. Collaborate with leading experts in Artificial Intelligence and Machine Learning at ANU and Defence stakeholders. About the
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systems (such as RedCAP), Endnote files, and databases Demonstrated experience with data analysis, visualization, and building machine learning models in programming language such as Python or/and R
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completion) in computer science, electrical engineering, AI, machine learning, remote sensing, robotics, or a closely related discipline. Demonstrated expertise and research track record in deep learning and
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manufacturing principles. Experience with machine learning methods and integration into hybrid modelling systems Demonstrated ability to clearly communicate research concepts and results in high-quality journal
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an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and agricultural datasets proficiency in R and/or
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: Essential criteria A doctorate (or will shortly satisfy the requirements of a PhD). The doctorate must be in a relevant discipline area, such as statistical machine learning, computational and quantitative
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focus on processing and utilising machine-learning techniques to analyse large volumes of data from sensors installed in Phase 1. The aim will be to merge the QC points and tracking system developed in
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focuses on scalable, AI-driven decision-making in collaboration with defence industry partners and an academic partner at the University of Melbourne. One role focuses on machine learning for decision
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: Using big data insights to optimise the manufacturing process The second phase of this project will focus on processing and utilising machine-learning techniques to analyse large volumes of data from