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these influence immune function and disease. The project combines biochemistry, molecular and cellular biology, and in vivo models to deliver a comprehensive picture of immuno-metabolic function during inflammation
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technical knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques
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such as COMET and nCounter assays will be used to triangulate host immune responses to invasive bacteria. The successful candidate will also use our established infection model of patient-derived gastric
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). This global project aims to assess the behavioural and social drivers of vaccine uptake and to identify both supply- and demand-side barriers to immunisation. The post-holder will contribute to modelling
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Computational Chemistry to develop accurate models of time-resolved X-ray photoelectron spectroscopy (TRXPS) experiments of gas phase molecules. The successful candidate will be based in Southampton and work in
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We are seeking a highly motivated Postdoctoral Research Associate in the field of Theoretical and Computational Chemistry to develop accurate models of time-resolved X-ray photoelectron spectroscopy
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Type of Contract: Full time - Funding limited for 34 months The postdoctoral research fellow will support the delivery of a variety of critical research tasks to deliver the JUST-Systems programme
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Obstructive Pulmonary Disease (COPD). This position offers an exciting opportunity to contribute to impactful applied health research and influence future models of care for people living with COPD. The post
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the Edge AI foundation and various industrial partners. We are seeking candidates with a doctoral degree and substantial experience in Persuasive and Edge Computing or related areas. Essential expertise
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relevant field, including statistics, mathematics, computer science, epidemiology. Strong mathematical and quantitative skills. Experience in the implementation of mathematical or statistical models and