10 modelling-complexity-geocomputation PhD positions at Queensland University of Technology in Australia
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work on the research of theory and modelling in photochemistry to understand the fundamental mismatch between photochemical reactivity and molecular absorptivity , which is an exciting and unexpected
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to manage large complex engineering problems with thousands of components, relationships, constraints and details. This project will use this systematic, searchable, analysable modelling technique to examine
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CAD modelling CFD Demonstrated ability to work independently and to formulate and tackle research problems. Demonstrated academic research experience as evidenced by publications in high quality
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applied mathematical modelling machine learning multi-fidelity modelling numerical methods. Demonstrated programming ability (MATLAB/Python/C++) and enthusiasm to learn PyTorch. Previous experience in one
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related policy/business discipline. Have familiarity with empirical research methods, experimental design, or technical modelling relevant to your field. Having a demonstrated publication record or
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in finite element modelling and machine learning (desirable). How to apply Apply for this scholarship at the same time you apply for admission to QUT's Doctor of Philosophy . *The first step is to
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the service life of infrastructure. The research content includes: real-time data analysis and modeling pipeline network dynamic prediction and state identification optimisation algorithm and high-performance
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modelling, and resilience science. You will work within an interdisciplinary team, engage directly with communities, policymakers, and practitioners, and contribute to co-designed toolkits and guidelines
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applied physics other related disciplines. Demonstrated knowledge in at least one of the following areas: porous media flow computational fluid dynamics (CFD) pore-network modelling lattice Boltzmann method
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expertise: Bioinformatics experience A PhD student with bioinformatics experience will generate and analyse complex microbiome sequence data to complement existing rumen microbiome datasets. Via multi-omics