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and correlates will differ between younger adults and the elderly, and females compared to males. Methods: The laboratory uses world-class big-data ‘omics’ analysis of blood immune cells, including
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information systems) Have a strong background in quantitative research and methods, including handling large datasets Have a sound understanding of statistics and experience using multi-level statistical
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with large-area, ultrathin thickness, and high flexibility, understanding their plasmonic enhancement mechanisms, and construction of artificial leaves to perform the solar-to-chemical conversion, which
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for suitability. Project supervisor: Inge Koch [email protected] This project includes big functional data from proteomics MALDI mass spectrometry imaging with an external supervisor and data expert from South
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-biofouling of large scale surfaces used underwater. This Project combines expertise in the fabrication of nanomaterials, including magnetic nanostructures (Fe3O4, Fe3O4-Au core-satellites, etc.) using laser
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variational inequalities. The newly developed algorithms will be applied to large-scale optimisation problems arising in federated learning. The PhD student will work under the supervision of Dr Minh N. Dao
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Successful candidates will be expected to have a solid background in finance and sustainable finance, research methods, strong data analytic skills, and experience working with large datasets. RMIT
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under 'Admissions' in PhD (Mathematical Science). Have a Master, first-class Honours or equivalent in one of the disciplines of statistics, mathematics, or data analytics/science. Excellent programming
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Autonomous methods for fault or anomaly detection and classification of PV plants with high accuracy are necessary for the monitoring of large-size PV power plants. Objectives also include other
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pipelines to analyse big volumes of metagenetic data. The analysis pipeline will roughly use packages such as trimomatic, FastQ screen, centrifuge/kraken and many more. The PhD candidate: The preferred PhD