171 phd-in-mathematical-modelling-population Postdoctoral positions at University of Oxford
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Leedham (colorectal cancer biology), Dan Woodcock (cancer genomics), Helen Byrne (mathematical modelling), and Jens Rittscher (computational pathology and imaging AI), offering a unique opportunity to work
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base, the partnership will bring together the University of Oxford’s expertise in statistics, mathematics, engineering and AI with industry scientists. Within the partnership, small research teams will
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, Oxford, Leeds, Reading, and Birmingham) and international (Utrecht University, ETH Zurich, Université Catholique de Louvain, etc.) scientists to use new modelling resources and methods to elucidate drivers
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and optimising assays aimed at target validation; principally through immunogenicity assays in animal models. You will also conduct experiments aimed at understanding the tumour-immune microenvironment
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cancer progression, immune evasion, and therapeutic resistance. We place a strong emphasis on the use of spatial biological approaches applied to human tumour models including organ/tumour perfusion, slice
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level of detail extracted from these experiments. As part of this role, you will work closely with other researchers to translate these experimental results into our numerical models, helping to improve
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researchers to translate these experimental results into our numerical models, helping to improve their predictive capability. You will help ensure a healthy and vibrant research environment within the Impact
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to the 4th February 2026. You will be investigating the safety and security implications of large language model (LLM) agents, particularly those capable of interacting with operating systems and external APIs
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combine a series of interdisciplinary approaches ranging from experimental embryology and fluorescent microscopy to mathematical modelling. The lab is highly interdisciplinary and collaborative. You will
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an available option. Applicants with a range of academic subject backgrounds are welcomed, including natural sciences, epidemiology, engineering, statistics and applied mathematics with experience and