16 condition-monitoring-machine-learning Fellowship positions at The University of Queensland
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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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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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), 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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, and monitoring progress, adopting accepted project management techniques. Experience in liaising and collaborating with external agencies to develop cooperative research initiatives. A strong record of
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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
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discipline, including publications in high-quality peer-reviewed journals and presentations at major conferences. Demonstrated high-level mathematical and computer programming skills. Where PhD has been
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biology is highly desirable. Practical experience in bioprocess engineering, including operation of bioreactors, optimization of microbial growth conditions, and management of continuous or batch processes
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geomechanics, or ability to quickly acquire relevant domain knowledge. Proficiency in high-performance computing (HPC) for large-scale parallel simulations. Experience with advanced constitutive models and their