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Scientia Professor, the Post-Doctoral Fellow will undertake advanced data management and statistical analysis of large, complex linked datasets, contribute to rigorous study design, and ensure high standards
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projections. Demonstrated experience in working with large data sets. Strong programming skills (e.g. Python, R) and ideally experience in high-performance computing. Demonstrated ability to work in a team
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large organisation with a diverse and talented community; a community doing extraordinary things. But what makes us different isn’t only what we do, it’s how we do it. Together, we are driven to be
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working on clinical trials, as well as contributing to the development of data-driven interventions and stewardship models to enhance patient outcomes after surgery. This position is an opportunity to work
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developed by extending the existing platform through adoption or development of new components. Phase 2: Using big data insights to optimise the manufacturing process The second phase of this project will
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: Using big data insights to optimise the manufacturing process The second phase of this project will focus on processing and utilising machine-learning techniques to analyse large volumes of data from
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statistical genetics techniques to analyse large-scale single-cell and genomic data. Data Analysis: Process, analyse, and interpret high-throughput single-cell and genomic data to derive biologically meaningful
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the information encoded in our genome to better diagnose, treat, predict and prevent disease. From the individual patient with rare disease, to the many thousands affected by complex, widespread illness, we
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point of contact Experience working with Global Burden of Disease (GBD) Study data with successful publications utilising this data Experience designing and creating self-help tools for people who suffer
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Health to work on a large multi-organisation project called Development of a Soil Health Framework for Australian agricultural production systems which is funded by the Grains Research & Development