45 phd-position-computer-science-"IMPRS-ML"-"IMPRS-ML" Fellowship positions at Monash University
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engineering. Be part of ARMI’s mission to address the unanswered questions with a multi-centre, cross disciplinary and highly focused approach, for the development of innovative clinical protocols as
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meaningful program of work that includes literature reviews, field research, data analysis and scientific writing. The role also involves preparing ethics applications and presenting research findings
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about us: https://www.monash.edu/medicine/her-centre About Monash University At Monash , work feels different. There’s a sense of belonging, from contributing to something groundbreaking – a place where
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, Civil and Environmental Engineering md.kamruzzaman@monash.edu Position Description: Research Fellow Applications Close: Wednesday 16 July 2025, 11:55pm AEDT Supporting a diverse workforce
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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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Diseases team and contribute to vital surveillance and data linkage work on respiratory viruses, including influenza and COVID. The position is responsible for managing data from sentinel systems such as
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passionate and dedicated Post-doctoral Research Fellow for a Level B research-only position. This role is part of an exciting project titled "The Dynamics of Agricultural Innovation in North India." As a
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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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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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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