16 condition-monitoring-machine-learning-"Multiple" Fellowship positions at The University of Queensland
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Sustainable Minerals Institute / Global Centre for Mineral Security Multiple full-time (100%), fixed-term positions for up to 2 years Base salary will be in the range $140,293.64 - $161,518.33 + 17
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techniques such as PCR, modular cloning, Golden Gate assembly, USER assembly, and CRISPR/Cas9-based genome editing; Sound understanding and practical experience applying the Design-Build-Test-Learn (DBTL
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, cluster randomised controlled trials implementation science, data linkage, data science, machine learning and artificial intelligence. In this role, you will have the opportunity to engage in a series of
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the application of AI and machine learning to identify novel therapeutic targets and advance precision medicine approaches. For further information, please click here to view the full appointment
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in advanced signal processing techniques and good understanding of emerging machine learning methodologies used in NDE. You will work in close collaboration with project partners at the University
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biosynthesis and the downstream extraction and purification processes. Key responsibilities will include: Research: Optimise bioreactor conditions and feeding strategies for maximum PHA production and monitor
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will include: Research: Lead at least two cohort studies (one that includes diverse pain presentations and a smaller cohort for more detailed analysis of a single pain condition), seek and manage
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), clinical trials, disease surveillance, and the use of novel methods including Bayesian network, machine learning, social network analysis and dynamic data visualisation tools. Further information is
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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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role will involve isolating and cultivating microbes under varied conditions, extracting and characterising novel natural products, and analysing biosynthetic gene clusters (BGCs) to uncover