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MRC DiMeN Doctoral Training Partnership: Deconvoluting the spatial multi-omic landscape of high-grade glioma to target post-surgical residual disease MRC DiMeN Doctoral Training Partnership PhD
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honours degree in a discipline relevant to applied health research e.g. health sciences, psychology, sociology Essential Application A PhD in a topic related to applied health research (or equivalent
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engineering or a relevant discipline Essential Application Have completed, or be about to complete, a PhD in control engineering, mechanical engineering or a relevant discipline Essential Application Experience
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the application statement when you apply. Criteria Essential or desirable Stage(s) assessed at A PhD in a relevant health science such as psychology or medicine, and methodological discipline (applicants near
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to the preparation of scientific manuscripts, attend and deliver seminars/lab meetings Help supervise undergraduate and MSc students Advise PhD students as necessary as part of a collaborative research team. Carry out
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, Assembly, and Digital Engineering for Manufacturing PhD Research Project Directly Funded UK Students Prof Hassan Ghadbeigi, Dr Matthew Brown, Dr Kyle Marshall Application Deadline: 07 November 2025 Details
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honours degree and a PhD or equivalent experience. At least one of these should be in healthcare, health services research, a social science or a closely related discipline. Essential Application Possession
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Essential or desirable Stage(s) assessed at PhD, master’s or equivalent professional experience in a relevant area such as materials science, bioengineering, advanced manufacturing, dentistry Essential
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team to deliver the research aims. Contribute to supervision of PhD and MSc students working in the group. Carry out other duties, commensurate with the grade and remit of the post. Person Specification
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for the Research Associate, Grade 7 level, position must have a PhD in a quantitative biology discipline, statistics or machine learning along with a proven track record of research using statistical modelling