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). This consortium pulls together expertise from all over the University, including in diagnostics, data science, drug and vaccine design, preclinical testing and clinical testing and brings it to bear on tuberculosis
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and social data with the aim to better understand energy use. The programme is an interdisciplinary collaboration between the School of Geography and the Environment (Oxford), the Department
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project to develop a systematic framework for reconstructing the evolutionary histories of pathogens. The role involves using viral sequence data and models of sequence evolution to investigate both
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, fluorescent microscopy, etc.), as well as extensive experience in quantitative proteomics (both sample preparation and data analysis) is expected. As a postdoctoral researcher, you are expected be able
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and leading a programme of numerical simulations relating to all aspects of our research on P-MoPAs; using particle-in-cell computer codes hosted on local and national high-performance computing
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technologies have exploded in recent years, generating multi-dimensional data from millions of cells and thousands of molecular features. Understanding these data and harnessing their insights remains an ongoing
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on transplant using multimodal medical data. You will be responsible for literature review, data cleaning, model development and implementation. You should possess a relevant PhD (or near completion) in
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qualitative social science research, such as literature reviews, surveys, interviews, thematic analyses, researching and writing up case studies, data presentation, etc. Candidates also proficient in
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detailed T-cell phenotyping and functional assays • Conduct mechanistic studies using molecular and cellular immunology techniques • Contribute to data analysis, presentation, and publication
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• Uncertainty quantification around LLMs • Constrained optimal experimental design (active learning) • Combining models and combining data / Realistic simulation of clinical trials • Developing