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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 10 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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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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radiation therapy treatments. At least one position will be offered to a researcher that has experience in deep learning and AI development and a willingness to apply these approaches in radiation therapy
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. Demonstrated ability to work as a member of a team in a supportive, inclusive and collegial manner. Proven computer literacy and proficiency in using various research and administrative software applications
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.) during medical imaging and radiation therapy treatments. At least one position will be offered to a researcher that has experience in deep learning and AI development and a willingness to apply these
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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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(e.g., Docker, Kubernetes, cloud/edge environments). Demonstrated expertise in AI, distributed computing, machine learning, or systems software design. Strong background in software engineering
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, are strongly encouraged to apply. Learn more about the ADM+S Centre here: https://www.youtube.com/watch?v=AkyZpYjxNBc To be successful for this position, you'll have: A PhD in a relevant discipline area, such as
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a culturally and linguistically diverse background, are strongly encouraged to apply. Learn more about the ADM+S Centre here: https://www.youtube.com/watch?v=AkyZpYjxNBc To be successful
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of published research relevant to clinical medicine, especially those using quantitative research methodologies. Experience and demonstrated achievement in University-level teaching and learning. A record of