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themes: development of innovative tools for single-cell and spatial analysis of lipid immunometabolism (Subprojects 1–5); molecular pathways linking lipid metabolism to immune activation and
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workflows. Qualifications PhD in Computational Biology, Genomics, Biomedical Engineering, Neurosciences, or related fields. Proven expertise in single-cell or spatial transcriptomic/proteomic data analysis
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areas of sustainable intensification of agriculture, climate mitigation and adaptation, livestock systems, and healthy and sustainable diets, providing analysis and insights aimed at helping to shape
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Quantification and spatial analysis of immune biomarkers (e.g. TILs, immune subpopulations) Development of AI pipelines for automated analysis of whole-slide images Predictive modelling of relapse and metastasis
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques
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TLDR: Build the data backbone for the next era of AI-powered spatial biology. Please include a cover letter with your application detailing your qualifications and experience for this position
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such as R, SAS, ArcGIS, SQL, Python and AI tools. Conducts geospatial and epidemiologic analyses relevant to the catchment area to assess cancer outcomes, spatial patterns, temporal trends, and disparities
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proteomics. Experience with spatial data analysis including multiplex IHC and CODEX. Familiarity executing bioinformatics pipelines in local UNIX/Linux environments and cluster execution (LSF). Proficiency in
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offshore wind farms or tidal stream arrays. Such wakes are important to understand and predict because of their potential to impact the energy supply from such renewable energy projects. Analysis will be
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analysis pipelines and to integrate imaging data into computational models. It is an opportunity to work with research groups across the Cell-Matrix Centre and the Bioimaging Facility. The appointee will be