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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 2 months ago
machine-learning methods to investigate the deep-time controls on copper mineralisation. The role will involve developing reproducible computational workflows, generating predictive maps of copper
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via the academic promotions process. About You Completion of a PhD in a relevant health-related discipline. Research expertise in epidemiology and/or quantitative, qualitative or mixed methods
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inclusion , fostering an environment that mirrors our wider community. We're committed to attracting, retaining, and promoting diverse talent. If you require an alternative method to submit your application
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the discipline area. Direct experience with flavour chemistry, sensory evaluation, methods development (LCMS, GCMS), and multivariate analysis related to beef and meat products is highly desirable. Evidence of
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services research with, and on behalf of the Professor of Allied Health and Active Ageing. Within this exciting Medical Research Future Funded position, you will have the opportunity to work across elements
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, retaining, and promoting diverse talent. If you require an alternative method to submit your application due to accessibility needs or personal circumstances, please contact talent@uq.edu.au . Other
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qualifications in related fields (e.g., neuroscience) experience in quantitative methods relevant to immune science experience in clinical research with strong data analytic skills with an interest in training in
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. Experimental experience in device fabrication and testing across one or more relevant material and photonic platforms, with the ability to adapt methods to deployment‑oriented requirements. Strong capability
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% teaching component.) The Role The Macquarie University Lighthouse Fellowship (MQLF) supports researchers within three years of the award of their PhD who have an outstanding track record (relative
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bioinformatics and computational biology, including the handling of large-scale genomic datasets. Expertise in the evaluation of novel predictive genomics methods, and experience with whole genome assembly