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data science methods to build explainable and integrated machine learning models that can be utilised by health services to make real-time, data-informed clinical decisions in youth mental health care
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Acknowledgement of Country CSIRO acknowledges the Traditional Owners of the land, sea and waters, of the area that we live and work on across Australia. We acknowledge their continuing connection
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Metallurgy and Corrosion cluster, working within a multidisciplinary team spanning theory, advanced characterisation, and computational modelling. This environment provides an excellent platform for developing
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), housing types and/or ownership models Exploring the impacts of prepayment meters in remote communities and identifying alternatives Investigating the health-related impacts of energy insecurity, through
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of potential options for implementation. This will include prototyping and testing of various implementation options, analysis and documentation of results. Project 2 - Future Power System Modelling: As part of
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(e.g. compound muscle action potential). Experience in generating and/or working with both animal models and human models of Motor Neuron Disease Ability to work collaboratively with colleagues Ability
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publication Strong programming skills and familiarity with machine learning or finite element modelling Not currently receiving another scholarship of equal or higher value Application process Future student
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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are recruiting three PhD students for the following Projects: Project 1: Unifying on-farm data and crop models to enhance tactical crop decisions Summary: Despite the increasing availability of on-farm data and
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modelling astrophysical phenomena. The PhD project will focus on developing theoretical methods to generate accurate data to meet this demand. The student will gain expertise in high-performance computational