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Field
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successful candidate will work in the AI in Medicine Lab, applying machine learning to cutting-edge problems in cancer genomics, transcriptomics, spatial transcriptomics, and large-scale bioinformatics
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analyses of kidney tissue by immunohistochemistry (IHC), immunofluorescence (IF), RNA-sequencing, scRNA-seq and spatial transcriptomics; analysis or urine and blood samples. - Perform tissue culture
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tissue homeostasis and disease, especially using single-cell and spatial omics approaches. Our research is highly interdisciplinary, interfacing tumor biology, immunology, and neuroscience, with a strong
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eager to explore the interface between cell wall mechanics and soil physical properties applying spatial gene regulation to understand root biomechanics. This position is ideally suited to a highly
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-clinical models and multi-omic approaches. The proposed research involves the application of state of the art methods such as single cell RNA-Seq/ATAC seq/spatial transcriptomics on preclinical animal models
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microscopy, spatial transcriptomics and in vitro and in vivo models to study brain tumor cell-cell interactions and the organization of the cancer ecosystem. We strive for highly collaborative and inclusive
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rooms are optimised for listening tests on loudspeakers to BS 6840-13 / IEC 268-13, and is equipped with state-of-the-art multi-channel spatial audio systems such as ITU standard layouts and Ambisonic
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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“Enhancing Machine Learning Approaches for Spatially Dependent Data in Fisheries and Environmental Research” (CMAT, University of Minho), reference 2024.15617.PEX, financed by national funds through
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develop an industry-funded soil health framework for Australian agricultural production systems. The CERC Fellow will be a broad soil systems thinker and have a strong interest in using spatial approaches