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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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for Big Data Research in Health (CBDRH), strategically partnered with the School of Clinical Medicine (Women’s Health) at the University of New South Wales, Sydney (UNSW). This positioning places the NPESU
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most important summer cereal, producing large amounts of stem biomass with untapped potential for renewable energy and sustainable materials. In this role, you will explore genetic diversity in stem
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candidate will contribute to projects on flow-ecology relationships and habitat suitability modelling for freshwater organisms and will be comfortable working with large datasets, including spatial
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working with large datasets, including spatial information, hydrologic data and survey records. Skills and Experience To be considered for this position, you will have: completed a PhD or equivalent
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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
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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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, filter handling, mineralogical or particulate sample processing). Strong data-management skills, including experience with large analytical or imaging datasets, file organisation, naming conventions, and