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data requirements, and lower costs for large-scale modelling tasks. PINNs enhance predictive capabilities and efficiency by combining data-driven methods with physical principles. Unlike traditional
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: Contact Person: Dr Nataliia Sergiienko School/Discipline of: Mechanical Engineering Tel: (08) 8313 6465 Email: Nataliia.sergiienko@adelaide.edu.au EXPRESSIONS OF INTEREST: Expressions
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cutting-edge research in biochemistry, molecular biology and plant science. The student will be supervised by Dr Tatiana Soares da Costa based at the University of Adelaide. Dr Tatiana Soares da Costa is
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: Prof David Lewis and A/Prof Phil van Eyk School of Chemical Engineering School of Chemical Engineering Email: david.lewis@adelaide.edu.au ; philip.vaneyk@adelaide.edu.au Applying: Expression of
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mathematical integration, with the desired outcome being to increase the water and/or nitrogen use efficiency of Australian cropping systems. We propose to use a range of emerging data science approaches within
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seismic data to produce 3D geophysical models that will constrain the subsurface extent of rock units that might be suitable for hydrogen generation via radiolysis and/or serpentinization and identify
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of better prevention and public health policies in rural and remote areas. This position will contribute to the design of qualitative research components, coordinate qualitative data collection from rural
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the development of predictive models to anticipate potential failures. Additionally, the project will facilitate the transfer of this technology to industry, while also advancing academic knowledge
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and rural health staff informatics, coordinate the writing of ethical applications and reports, and contribute to writing publications and external grants. To be successful you will need: A PhD in
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will provide high quality data comprising images, text, and detailed locational information for 667 names/toponyms given to localities on the Australian coast by d’Entrecasteaux and Baudin. The expected