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Field
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research aimed at quantifying or modelling the state and function of soils across spatially variable landscapes. Applied experience in using statistical and mathematical tools to analyse and interpret soil
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available at the John Curtin School of Medical Research (JCSMR), within the ANU College of Science and Medicine. This role supports computational analysis of high-resolution spatial transcriptomics and single
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remission as a model of flare vs remission with a 50% relapse rate. Initial data analysis has taken place using the 10x Chromium single cell sequencing identifying putative cellular subpopulations
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include: (i) Analyze long-term changes in climate, land cover, and population to identify distinct locations across multiple spatial scales undergoing varying rates of change; (ii) Evaluate associations
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models developed rapidly, and you will support work to ensure that the methods can be run, evaluated and improved by partners in African universities and weather services. A significant part of your work
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model for innovative medical education and a centre for transformative research. The School’s primary clinical partner is the National Healthcare Group, a leader in public healthcare recognised
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or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Core job duties include: (1) Designing, implementing, and
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multi-omics data integration (e.g.: ATACseq, RNAseq, single cell RNAseq and spatial-omics platforms like CODEX/mIF or 10x Visium). The fellow will work with several research collaborators in the EECS
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architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will conduct research related to creating or testing deep learning models
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, and utilize the state-of-the-art multi-scale atmospheric models, along with measurement and statistical approaches, to understand the mechanisms that drive the climate-air quality interactions, and