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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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open-source tools and training modules for global utility adoption. The framework combines physics-informed graph-neural-networks (GNNs), diffusion model, and explainable reinforcement learning (XRL
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Full-time onshore enrolment Strong background in fluid-structure interaction or in systems and control Solid background in mathematics (theories in both ordinary and partial differential equations
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-preserving trustworthy distributed machine learning. In this research, the successful candidates will focus on mathematical backgrounds involved in differential privacy to devise novel scalable approaches
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different from when core theories about the role of media in society were established. We are awash in data about clicks, views, and likes, but we lack evidence about what people now do with media, how
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). "Statistical field theory applied to complex networks” "Quantum geometrogenesis – Graph theoretic approaches to building spacetime” web page For further details or to discuss alternative project arrangements
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These scholarships are funded by the Australian Research Council (3) and the University of Adelaide (1) to support 4 full-time PhD students who are undertaking research in the field of battery recycling: Project 1 (2 PhD students): Cost-Efficient Direct Recycling for Metal Oxide Cathode...
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PhD in the epidemiology field Eligibility criteria Undergraduate degree with Honours or Masters in Science with units in mathematics and/or biostatistics Demonstrated coding experience: either
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@rmit.edu.au Please send your CV to akram.hourani@rmit.edu.au Required Skills: Programming and simulation: strong experience in Python or MATLAB. Mathematical modelling: probability, optimization, or multi-agent
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open to Australian citizens who can meet the requirements for PhD admission at the University of Adelaide, and who can demonstrate suitable experience in Mathematics, Physics and/or Computer Science