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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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laboratory experience, such as: DNA/RNA extraction, PCR, cloning, virus production, ELISA, etc. Computational biology laboratory experience, such as: bulk- singl cell- or spatial-transcriptomic data analysis
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-026-01104-3. Preferred skills/knowledge Applications are invited from graduates in quantitative disciplines such as computer science, AI, and mathematics, but we also encourage applications from
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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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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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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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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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major £10 million national research programme bringing together leading experts in cancer epidemiology, biostatistics, artificial intelligence, and health data science. THE PROJECT: Early cancer diagnosis
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biostatistics. We develop, apply and promote innovative statistical and data science approaches to advance biomedical science and human health. The BSU current research portfolio is organised into five main