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of Information Technology is seeking a Level A Research Fellow to join the Department of Data Science and Artificial Intelligence (DSAI). This role offers an exciting opportunity to contribute to cutting-edge research
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University's Department of Economics is seeking a Research Fellow to join the project titled “Women in STEM: The Longer-Term Effects of Teachers.” This research initiative aims to understand how teacher
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and oral communication skills, including drafting of academic papers and grants High level computer skills with software skills such as Microsoft Office, SAS, SPSS If this sounds like you, we highly
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of Civil & Environmental Engineering is seeking an exceptional early-career researcher to join a nationally significant ARC Discovery Project exploring the sustainable size of cities and the transformative
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independently and as part of an interdisciplinary and cross-cultural team, possess excellent organisational and communication skills, and work collegially with other staff. Advanced computer skills, including
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delivery of clinical research that directly impacts real-world care. This role is ideally suited to an early career researcher or health sciences graduate with experience in clinical data collection and
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team as an A Level A Research-Only Academic and contribute to cutting-edge studies in the field of epigenetics, multi-OMIC science, ageing, and exercise. In this role, you will support the university's
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leading medic-legal and engineering researchers on real-world crash data Join Australia’s only academic department embedded in a forensic medicine institute The Opportunity Are you a proactive early-career
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multidisciplinary in nature, cutting across statistics, econometrics, operations research and data science. Specific project outcomes relate to multiobjective optimisation, expected shortfall and conformal inference
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implementation science. We are now seeking an exceptional Research Fellow (Level B or C) to lead innovative methodological research in statistical theory for adaptive variants of longitudinal cluster randomised