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for an enthusiastic and highly motivated Research Fellow to join the world-leading tuberculosis (TB) Modelling group at LSHTM. The successful candidate will be supervised by Dr Rebecca Clark and Prof Richard White and
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evasion thus supporting bacterial pathogenesis. We will characterise the antibacterial and anti-host activities of the T6SS and selected effectors using in vitro and in vivo models, deletion mutants and
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health researcher is sought with expertise in quantitative surveys, mathematical modeling in nutrition (especially in Sudan), and crisis clinical nutrition program management. The ideal candidate should
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project investigating mechanosensing in Diptera. This post will focus on using detailed wing geometry models and kinematic measurements in computational fluid and structural dynamics simulations to recover
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clinical endocrinology both paediatric and adult, genetic aspects and gene discovery in endocrine diseases, animal models, stem cells, signalling and cell biology. For further information visit: http
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regression models to complex forms of observational data, and of applying causal inference approaches to health data are essential. Experience of analysing time-to-event outcomes is desirable. This role offers
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responsibilities will include: Pre-registering data analysis plans; Leading and conducting advanced statistical analyses (e.g., twin/family designs, genomic and epidemiological methods, longitudinal modelling
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experimental approaches to develop and validate novel in vitro and ex vivo approaches that model arterial medial calcification without using any animal products. This work will represent an exciting step forward
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infrastructure enables recruitment of 200-300 severely injured patients annually as part of the ACIT study. We also have a well-established experimental modelling group with full ethical approvals in place for all
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responsibility for implementing a deep learning work-package as part of a Cancer Research UK-funded programme, developing an image-recognition model to identify morphological features corresponding to clonal