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an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and agricultural datasets proficiency in R and/or
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agricultural science with a quantitative focus (or an equivalent discipline) expertise in statistical and machine learning approaches, with the ability to apply advanced methods to complex environmental and
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focus on processing and utilising machine-learning techniques to analyse large volumes of data from sensors installed in Phase 1. The aim will be to merge the QC points and tracking system developed in
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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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, economic growth, inequality, and culture. The project draws on large-scale data collection and archival records, and applies a diverse set of methodologies such as text analysis and machine learning
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individuals. iPSC “Village” systems and CRISPR perturbation to experimentally dissect and validate gene function in controlled, scalable cellular models. Advanced computational genomics, machine learning, and
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draws on large-scale data collection and archival records, and applies a diverse set of methodologies such as text analysis and machine learning to assess the impact of colonisation. About you PhD in
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, machine learning, and causal inference frameworks that link genetic variants to cellular mechanisms and therapeutic opportunities. Our research spans immune biology, cardiac disease, neurodegeneration, and
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to fundamental research in forest imaging and 3D deep learning Base Salary Level A Step 6 – 8 $113,400 - $121,054 p.a + 17% superannuation About the opportunity We are currently seeking a Postdoctoral Research
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. This postdoctoral research position lies within the Centre for Augmented Reasoning (CAR), that forms part of the Australian Institute for Machine Learning (AIML). It offers the opportunity to carry out research