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help supervise associated PhD students. The successful candidates will join large, supportive research teams led by Profs Knight, Screen and Connelly all working collaboratively at Queen Mary. This is an
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project on Addressing socio-technical limitations of Large Language Models (LLMs), particularly for medical and social computing (https://adsolve.github.io/ ). The role involves developing methods
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will plan and conduct experiments, generate high-quality data, prepare publications, make presentations and help supervise associated PhD students. The successful candidates will join large, supportive
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researchers and industrial collaborators on the research project. About You The candidate should have a PhD (or close to completion) in a biological, biomedical or closely related science. Previous work
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and culture or ex vivo artery culture is highly desirable. About the School This post is within the School of Engineering and Materials Science, a large School with 100 academics and 60 postdoctoral
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Technology Laboratory (DSTL), Electromagnetic Environment (EME) Hub. About You Applicants should have a PhD in modelling hypothetical scenarios, with and without data, for structured decision-making under
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existing therapies. About You This project would be suitable for an individual with a PhD in immunology, cell biology, metabolism or related area. Candidates should have expertise in a range of cell biology
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Right to work: Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. For further information visit the UK Visas
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will deliver projects that leverage large-scale electronic health record data and rich cytometry data derived from full blood count analysers to develop and refine machine learning models to improved
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at the Barts Cancer Institute (Queen Mary University of London). This role will involve analysing existing spatial-omics data sets and developing novel computational tools to understand the risk of developing