352 algorithm-development-"Multiple"-"Prof"-"Prof"-"Simons-Foundation" "U.S" positions at Monash University
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site submissions and support the implementation of PROMS and New Zealand integration Leading development of key registry outputs including reports, communications and the Annual Public Report Managing
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meetings A working lunch at in-person meetings Training support for your role as an AEC member The opportunity to understand the cutting-edge science activities being performed at Monash Development
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program. In this dynamic research environment, you’ll support key activities such as coordinating preclinical trials, conducting experiments with specialised technologies and developing procedures
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complex administrative or technical functions and business systems involving significant resources Lead and develop a high-performing team with a strong customer focus Foster continuous improvement in
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). We’re offering an exciting opportunity for a Level A Research Assistant who will contribute to the University’s research efforts and develop their scientific expertise through focused and meaningful
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advice and in-depth analysis of complex health data. The role supports both independent and team-based research activities, including the development of manuscripts, conference presentations and funding
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% employer superannuation Operate and maintain cutting-edge flow cytometry instruments Train users and assist experimental design and analysis Manage daily lab operations, bookings and safety protocols
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expert guidance, analysis, planning, and modelling to ensure the University remains agile, competitive and financially sustainable. Lead the annual delivery and development of the University’s central
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Monash Mentor Scholarships The Monash Mentor Scholarship is designed to recognise excellence of students participating in the Access Monash Mentoring program. Mentors have the opportunity to develop
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learning is vulnerable to spurious correlations, novel causal discovery and inference methods will be developed to identify and reason over causal relationships among all associations from fused data. As the