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autonomously and collaboratively whilst prioritising tasks in a fast-pasted environment. You hold the following skills & experience: An understanding of the project/program life cycle and the development and
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policy and system controls, while monitoring outcomes and escalating exceptions. Providing guidance, training, and quality assurance to Admissions Officers to support capability development and consistency
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Apply now Job no:539036 Work type:Full Time Location:Sydney, NSW Categories:Marketing / Communications, Business Development / Industry Engagement Employment Type: Full time, fixed term contract
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University of New South Wales | Canberra, Australian Capital Territory | Australia | about 3 hours ago
in an effective manner, using appropriate guidance, motivation and development/coaching to ensure staff are supported to do their best work. At level 6 (in addition to level 5 above): • A demonstrated
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, improving and transforming lives through excellence in research, outstanding education and a commitment to advancing a just society. Why your role matters The Head of Data Platforms leads the development and
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for this position. Candidates must hold unrestricted work rights to be considered for this position. Position Summary: The Practice Manager leads the overall operation and strategic development of the UNSW Lifestyle
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, contributing to impactful outcomes in the field. Supervision and Researcher Development: Support and mentor Honours and HDR students, while contributing to team development, recruitment, and performance
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development and monitoring of Transnational Education (TNE) initiatives to ensure alignment with the University Global Strategy’s operational and strategic goals. Oversee the end-to-end delivery of TNE projects
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the development of Explainable AI Systems that can provide explanations of AI agent decisions to human users. Past work on plan explanations primarily focused on explaining the correctness and validity of plans. In
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of the development of connections in these biological neural networks and their spontaneous and stimulation-driven formation. These models will provide a framework for simulating and testing biological neural networks