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quantitative readouts using methods such as transcriptomics, high-content imaging, flow cytometry, proteomics, single-cell analysis, drug response or other molecular/cellular phenotypes. Collaborate across UNSW
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tumor microenvironment (TME). Lead the analysis and biological interpretation of single-cell RNA sequencing (scRNA-seq) data and integrate genomic, transcriptomic, and clinical datasets to address complex
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working with human date and experience analysing large-scale omics datasets (e.g., proteomics, transcriptomics, genomics, metabolomics, or related high-dimensional biological data) are desirable
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therapeutic approaches. Advances in proteomic, genomic and transcriptomic technologies offer powerful opportunities to address these challenges. This fully funded studentship forms part of the LifeArc Centre
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and apply machine learning and deep learning models to analyse complex, multi-dimensional datasets, including spatial transcriptomic, proteomic, and single-cell sequencing data. The candidate will
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of muscle hypertrophic growth; 2) identifying the glucose transporter(s) activated by resistance training in muscle; and 3) examining the connections between glucometabolic flux and the muscle transcriptome
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, and computational methods for analysing complex biological datasets. Experience with single-cell and spatial omics technologies, including transcriptomics, metabolomics, or multi-omics data integration
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computing skills to handle datasets of whole transcriptome RNA sequencing, ATAC sequencing, and whole genome sequencing. The objective of this position is to complete data analysis and integration of ATAC and
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new method to study human pancreatic tissue slices using high-resolution calcium imaging in combination with spatial transcriptomics. The researcher will develop methods to improve the viability
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opportunities to use approaches including GWAS, Mendelian randomization, transcriptomics and multi-omics analyses alongside functional genetic studies in Drosophila. Particular areas of interest include