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an Australian citizen, an Australian Permanent Resident or a New Zealand citizen. For further information, please follow the link for each Graduate Research Degree ● 3291 - Doctor of Philosophy ● 3292 - Master of
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is highly complex. For the proposed PhD project, experimental data are already available that bring together maps of orientations of such crystals together with the deformation pattern generated during
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linked pages are in Chinese. You may wish to utilise the 'Translate this page' function within Google Chrome if you require an English translation. This scholarship is also available to students
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academics into its Software Systems and Cybersecurity and Data Science & AI Departments. The Department of Data Science & AI is seeking a Teaching & Research academic working in Large Language Models and
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● Electrical and Computer Systems Engineering ● Materials Science and Engineering ● Mechanical and Aerospace Engineering For further information, please follow the link for each Graduate Research Degree ● 3291
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candidates holding (or eligible to obtain) a valid student visa. Candidates with strong academic and research track records are particularly encouraged to apply. For further information, please follow the link
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shape health, employment, and wellbeing outcomes for individuals and families. The successful candidate will join a highly collaborative research team using linked employer–employee administrative data
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information, please follow the link for each Graduate Research Degree ● 3291 - Doctor of Philosophy Candidate Requirements Applications are invited from candidates with backgrounds in Materials Science
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Indigenous students in any of the Faculty’s disciplinary fields. For more information, please visit www.monash.edu/medicine/indigenous-recruitment . These academics would have a healthcare background, be
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opportunities for a dynamic, ‘Learning Health System’ – where data can be harnessed to inform real-time and personalised decision-making. Existing linked administrative databases already capture Australian women