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Field
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performed are: o Extraction and purification of RNA from human biological samples. o RT-qPCR for the quantification and validation of non-coding RNA candidates for biomarker. o Transcriptomic analysis using
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variation as an anchor for causal inference across molecular layers (genomics, transcriptomics, proteomics, and metabolomics), enabling more principled identification of disease-relevant proteins and pathways
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, CRISPR-based approaches, transfection/transduction, and in vitro bacterial infection models. Apply omics approaches (e.g., epigenomics, transcriptomics, proteomics) to support drug targetidentification and
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PTLD patient and control patient blood and are performing single cell spatial transcriptomics to analyse the immune microenvironment of PTLD tumours. The appointed candidate will create and contribute
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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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/Engineer 3 (RS/E 3) in the Zhu Laboratory leverages state-of-the-art microphysiological systems, imaging technologies, and single cell RNA sequencing and spatial transcriptomics to study host immune
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capabilities to a variety of research platforms, including genomics, proteomic, transcriptomic, analysing microscopy, imaging, and clinical data, and developing the tools and pipelines that help our researchers
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leading role in research development, research articles, and proposal writing in transcriptome and metabolomics research. The successful candidate will contribute significantly to expanding the laboratory's
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biology, microbiology, genomics, transcriptomics, and proteomics techniques. - Collaborate with in-laboratory microbiologists, virologists, parasitologists, vector biologists, and bioinformatics
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning