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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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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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of a team, are also requirements of the role. About the School This post is within the School of Engineering and Materials Science (SEMS), a large School with 108 academics, over 250 PhD Students and
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work. The candidate is expected to support current PhD students in the group, write and publish research papers. About You Candidates should have a PhD in mechanical engineering, orthopedic engineering
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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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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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biomedical data scientist / computational biologist to join our highly collaborative team at QMUL. PhD (or close to completion) or research qualification/experience equivalent to PhD level in the relevant
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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