26 linked-data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions at University of Sydney
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of Mathematics and Statistics, Camperdown Campus Develop statistical and computational capability for multi‑omics data analysis and drug discovery Base Salary Level A/B $ $117,936 - $157,381 p.a + 17
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phenotyping, and neurobiological approaches to characterise the mechanisms linking sleep disruption and maladaptive eating behaviour. Working within a multidisciplinary translational neuroscience program, the
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epidemiological studies, potentially including analyses of existing linked population-based datasets, such as the Enduring Cancer Data Linkage (CanDLe) data assets actively contribute to all aspects of research
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Professor Anne Cust , conducts collaborative, multidisciplinary research with a public health focus. The team has strong links with Melanoma Institute Australia. This role will play a key part in supporting a
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that explore biological and behavioural factors influencing the onset, course, and response to treatment in young people with emerging mental disorders. Extensive collaborations with clinicians, data scientists
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of low birth weight. The successful candidate will support the delivery of impactful health service research through stakeholder engagement, research coordination, data analysis, knowledge translation and
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supervise and mentor honours, postgraduate and research students undertake statistical analysis and manage research data using REDCap, R, and other relevant platforms develop productive collaborations with
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underwater trial using a sovereign quantum vector magnetometer. The objective of the project is to use the gathered survey data to explore advanced platform compensation techniques, map matching and magnetic
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, ethics submissions, data collection, analysis, interpretation and dissemination of findings · collaborate with multidisciplinary teams of researchers, clinicians and health service stakeholders
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are particularly interested in: machine learning for molecular and omics data, including representation learning for biological sequences and structures, and the integration of multiple omics layers machine learning