-
, 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
-
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
-
improved methods to evaluate how these compounds distribute throughout the body and to better understand potential toxicities in normal organs. This project aims to establish an integrated preclinical
-
innovative statistical methods to real biomedical problems in order to deliver key insights into human health and disease. One highly competitive fully funded (covering University fees and stipend) three-year
-
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
-
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
-
tumorigenesis. Genomic and single-cell approaches may also be used, supported by collaboration with computational scientists. References/further reading Zhu H, Chan ASL & Narita M. The rise of RAS: how gradual
-
sequencing, flow cytometry, multiplex immunofluorescence, spatial transcriptomics, and standard molecular biology approaches. A computational component may also be available, depending on the skills and