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sequencing, flow cytometry, multiplex immunofluorescence, spatial transcriptomics, and standard molecular biology approaches. A computational component may also be available, depending on the skills and
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are essential, along with the ability to build and extend statistical pipelines. An interest in Bayesian inference applied to biology is also important. A background in computational proteomics or LC-MS/MS
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, regulated and exploited in cancer. The balance between method development, cancer biology and computational analysis will be shaped around your strengths and interests. Applicants from a physical-sciences or
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systems. The successful candidate should have a strong interest in mechanistic biology and in using experimental approaches to understand fundamental questions in cancer development. Computational
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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Health Data Science) Start date: January 2027 (or earlier) We are offering an exciting fully funded PhD studentship at the University of Cambridge, embedded within Cancer Data Driven Detection (CD3) - a
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. For further information regarding eligibility and PhD projects available please see PhD Programme MRC Biostatistics Unit (https://www.mrc-bsu.cam.ac.uk/phd-programme ) To apply please see the University's