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, and machine learning/artificial intelligence. This project focuses on the development of novel theoretical frameworks alongside cutting-edge computational techniques for large-scale optimisation
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, algorithmic methods, and machine learning approaches to advance research in melanoma and cancer biology. Specifically, you will support the major project “Predicting Early-Stage Melanoma at High Risk of
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 12 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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experience in using statistical and mathematical tools to analyse and interpret soil data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python
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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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: 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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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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geometry, and/or data science. Specific topics of focus include, but are not limited to, linear response, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics