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for large-scale multiomics datasets, particularly those derived from single-cell, spatial, and 3D genome experiments in a variety of disease and developmental systems, such as, gametogenesis and early
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to data analysis and biological interpretation. Role Summary Develop, implement, and apply advanced reproducible computational tools and workflows to process, analyse, and interpret large-scale LC–MS-based
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Your role We are seeking a coordinator to oversee incorporation of the increasingly large volumes of eukaryotic genome data into Ensembl. You will provide leadership and vision for our eukaryotic
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applying foundational methods for the synthesis of large genomes, specifically the human genome. For further background information see https://www3.mrc-lmb.cam.ac.uk/sites/synhg/ and Zürcher et al. 2023
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of large multidimensional real world data sets using Stata, R, Python, or similar. Knowledge of C++ would be advantageous but not essential. Ability to communicate complex information clearly. Excellent
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both product performance and patient outcomes. You will also have the opportunity to work with large-scale, multimodal healthcare datasets, and contribute to the development of an AI-native intelligent
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alliance that includes the University of Birmingham, Aston University, five major NHS partners, and the West Midlands Academic Health Science Network, and Big Data projects including the West Midland Secure
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challenge due to limited patient data—especially at the single-cell level—making traditional modelling approaches difficult. This project tackles that challenge by integrating multi-omics and clinical data
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oncology grouping sees close to 550 new cases of these cancers a year. There is a large active clinical trials programme that spans phase 1 to phase 3. These patients have a poor prognosis, and this clinic
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, guide experimental design and integrate large datasets Computational expertise in big data analysis Clinical research using patient cohorts to understand the cellular basis of disease, including