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currently seeking a Postdoctoral Research Associate in Spatial Omics and Statistical Data Science to join an ARC-funded Discovery Project developing statistical and computational methods for the analysis
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skills in metagenomics analysis Experience in applying AI-based analytical methods to research data. Proficiency in the use of R, REDCap, and Stata (desirable) for data management, statistical analysis
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or hybrid metal halide glasses, including melt-quenching and structure-property analysis. Hands-on experience with synchrotron techniques, from beamtime proposals through data collection to analysis
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-based research, ethics and governance processes, research data management, qualitative analysis, and the interpretation of findings to support project outcomes. Research Communication and Knowledge
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well as practical experience in dynamic model development and the analysis of large-scale biological data, who are excited to apply rigorous computational modelling approaches to real biological and
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include : Research: Lead innovative bioinformatics research focused on cancer epigenomics, gene expression, single-cell and spatial omics, and metabolomic data analysis. Develop computational tools
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biology, ecology, and epidemiology. These methods and applications will contribute to the Australian Research Council (ARC) Discovery Project “Efficient Bayesian analysis by exploiting fast approximate
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research program, including patient engagement, co-design activities, data collection, analysis and dissemination of findings. Your key responsibilities will be to: Coordinate and manage day-to-day
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Professor Jared Bronski at the University of Illinois, the successful candidate will help develop the generalised Maslov index into a practical tool for stability analysis. The project will
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changes this system because it can assist with writing, analysis, experimentation and review. Producing a plausible paper may become much cheaper while judging whether a result is important and reliable