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%). This post will support analysis of multiomics data (e.g. single cell and single nuclear RNA-seq, spatial transcriptomics, proteomics etc.) generated by the Borne Uterine Mapping Project (BUMP, focussed
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, early modern and modern world history. We are an intellectual home for scholars of every region of the world, who use approaches which range from local micro-histories to large-scale quantitative analysis
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for this role. This role will involve developing and applying analysis plans using a variety of advanced methods with the support of project supervisors. The postholder will have completed a PhD in a relevant
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the Spatial Biology Facility, you will lead the development and optimisation of high-resolution spatial biology and multi-omics data analysis pipelines. Your primary focus (80% of your time) will be on leading
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at the intersection of obstetrics and imaging. The successful candidate will play a key role in recruiting and supporting pregnant participants, coordinating MRI scans, and inputting into data collection and analysis
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orientation. Post-Doctoral level training in applied health research methods, excellent data collection and analysis skills, and experience of conducting and reporting mixed-methods research or evaluation
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publications and/or dissertation or equivalent evidence of expertise and completed research outputs Data science skills, especially data analysis and prediction modelling Proven ability to write code in Python
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, the development and fine-tuning of vision foundation models, multiple instance learning, survival analysis, and interpretable model development. You will also lead efforts in building multimodal deep learning