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the model, its numerical implementation in a finite element code, and its validation against experimental data in collaboration with experimental collaborators. You will also engage with the hub activities
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at the intersection of these research areas. You should hold, or be close to completing, a PhD/DPhil in mathematics, statistics, physics, engineering, data science, or a related field. Experience in cancer
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data independently. The post holder must also have a strong statistical background, with at least one recent publication in an internationally reputable journal. Application Process You will be required
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understand the links between maturational accounts of acquisition and the stability of morphosyntactic properties diachronically, with particular reference to French. Acquisitional data will be extracted from
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in vivo experiment documentation, sample inventories, and reagent information. You will also be responsible for ensuring that work in the laboratory is conducted safely and that work is undertaken
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fractionation (i.e. surface biotinylating, gradient centrifugation, and be proficient in advanced data analysis (i.e. R, Python). Excellent analytical, organisational, and problem-solving skills are essential
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expected that the successful candidate will contribute to the computational analysis of multi-omic data that will be generated during the project. The candidate must hold or be near completion to a PhD/DPhil
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which area you’re applying to (AI & Epidemiology, Phylodynamics & Human Mobility, Climate & Infectious diseases, Global health digital data science), as well as your preferred starting date. We
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targets, by leveraging high-dimensional big data (e.g. electronic health records, multi-omics and phenotypic) from large prospective biobanks including China Kadoorie Biobank (CKB) and UK Biobank (UKB), and
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in the Mathematical Institute (https://www.maths.ox.ac.uk/groups/mathematical-biology/infectious-disease-modelling). The postdoctoral researchers will develop data-driven mathematical models and