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the machine-learning infrastructure that integrates CFD outputs with biological datasets. This role bridges engineering, computational modelling, and experimental integration — ideal for someone
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comprehensive metadata schema is followed, consistent with relevant industry standards (e.g., ISO 19115). 2. Geospatial Analysis and Machine Learning: Develop and implement analytical tools and routines
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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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, 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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Academic Level B: Completion of a PhD in the field of Computer Science/Artificial Intelligence. Software engineering expertise, including design and implementation of AI-based models (machine learning, deep
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relevant to integration of machine learning and mechanistic models, and development of engines for efficient processing and visualisation of large-scale datasets and system geographic information and
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clarity, who are able to provide feedback in a timely fashion, and who are interested in developing and applying teaching strategies to support student learning in a range of different settings. Casual
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genetic variation into elite germplasm. This will require the integration and optimization of several technologies, including genomics, machine learning, genetic simulation, and speed breeding. This is a
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reputed refereed journals and presenting at conferences. Technical expertise in programming (e.g. python) and experience with high-performance computing are highly desirable. Experience in machine learning
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closely with researchers and developers, you will help identify challenges, propose solutions, and improve user interfaces based on feedback and testing. You can learn more about the Australian Internet