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(e.g., Microsoft Office) required. Knowledge in analytical and statistical software (e.g., GraphPad Prism, Microsoft Excel, etc.) required. Knowledge in Bio-Plex/Luminex software highly preferred
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chemical screens, single cell and spatial transcriptomics, proteomics, lipidomics, genomics, optogenetics and histology. Among the key duties of this position are the following: Undertake bioinformatics
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Training and optimizing ML algorithms on GPU hardware architectures, specifically NVIDIA based Working with geo-spatial data Statistics, multivariable calculus, and linear algebra Exploratory data analysis
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/DPhil with a significant computational and/or statistical element together with expertise in bioinformatics analysis of omics datasets, including single-cell and spatial multi-omics, and single-cell
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statistical learning frameworks to decipher single-cell and spatial transcriptomics data, with the goal of uncovering cellular and tissue dynamics underlying cancer, inflammation, and tissue senescence
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-seq, ChIP-seq, ATAC-seq, scRNA-seq, scATAC-seq, Hi-C, and spatial transcriptomics), apply statistical modeling and machine learning, and develop computational workflows using R, Python, and GitHub
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
spatial multi-omics data; AI-based modeling of protein structure and protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models
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the candidate will need to publish papers in English journal. 3. Have some experience conducting quantitative research (e.g., survey data collection, spatial analysis particularly in relation
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well as collaborative work on the high-resolution structure of the podocyte slit diaphragm. The Oxford Kidney Pathology (Bull) Group have an interest in structurally informed analysis of kidney spatial transcriptomics
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data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be