19 phd-position-in-database-modeling Fellowship research jobs at University of London
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responsibilities include data collection, analysis, and database management; preparing research findings for conferences, peer-reviewed journals, and reports; and conducting literature reviews to guide study
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fellow position within the William Harvey Research Institute at Bart’s and The London Medical School, Queen Mary University of London (QMUL). The post-holder will work on projects including the PinG study
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background in research in cardiopulmonary medicine or surgery and a PhD, although desirable, are not essential. We offer a generous reward package and benefits including: Competitive and attractive pension
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extracellular histone, a damage-associated molecular pattern that is implicated in adverse outcomes after injury. This project will include using a range of clinically relevant models as well as in vitro
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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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-led by Queen Mary University of London. PharosAI is set to revolutionise AI-powered cancer care, accelerating the development of breakthrough therapies, advancing clinical applications, and improving
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network of GP practices across the UK. The post-holder will have a postgraduate degree, ideally a doctoral degree, in a relevant topic and experience developing complex statistical models for real data
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to join the Environment & Health Modelling (EHM) Lab (https://www.lshtm.ac.uk/ehm-lab ) led by Prof Antonio Gasparrini. The successful candidate will work on the project CONNECT – Cohort and environmental
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postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as
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research project on cardiovascular risk prediction for people with immune-mediated inflammatory disease. The successful candidate will use advanced risk prediction methods to develop prediction models