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in one domain who are motivated to expand into the other. We also welcome purely computational applicants with interests in phylogenetics or advanced single-cell analysis, as well as purely
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at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing
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genomics, spatial metabolomics, single-cell sequencing, Optical/Photoacoustic imaging, seahorse, WB, RT-PCT, immunostaining and data analysis related to these assays. The experiments also include using cell
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genomics, spatial metabolomics, single-cell sequencing, Optical/Photoacoustic imaging, seahorse, WB, RT-PCT, immunostaining and data analysis related to these assays. The experiments also include using cell
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(with a focus on electrophysiology and sleep) and (ii) in spatial transcriptomics and high-resolution imaging. The selected candidate will work at the intersection of molecular biology and systems
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international collaborations Your profile at our group: analysis of high-throughput proteomic, (single cell) transcriptomic, digital spatial and other omics data in health and disease states multi omics data
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Analysis and Research Group (EPAR) Position Details Position Description The Daniel J. Evans School of Public Policy & Governance is a nationally top ranked public policy school, serving nearly 500 students
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exceptional postdoctoral research fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g
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) technologies (e.g. Geospatial Analysis, Geocomputation, Spatial Modelling, Robotics, Sensor Technologies, ML etc.) to carry-out rapid, wide-area mapping, 2.5D real-world modelling and highly automated feature
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procedures used in spatial data analysis and vegetation dynamics modeling is required. The candidate should have extensive practical experience in the use of R and GIS. Demonstrated skills of Bayesian modeling