18 condition-monitoring-machine-learning Fellowship positions at The University of Queensland
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure
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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 conducting human behavioural experiments or human-centered field studies. Demonstrated experience in using machine learning, such as deep learning for image processing, or natural
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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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projects. Working knowledge of machining processes and familiarity with CNC machining, concept design, prototyping, and developing designs through to commercialisation and production. Proficiency in CAD
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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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, 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