PhD position: Computational methods for inferring tumour evolution

The goal of this project is to tackle statistical and algorithmic challenges in understanding the evolution of tumours based on genomic data. The Markowetz laboratory has a strong track record in developing methods to dissect tumour heterogeneity and infer life histories of tumours. The project is for a student with strong mathematical and statistical skills. Initially the focus will be on developing new methods to trace clonal evolution across multiple samples from the same patients by integration information on single nucleotide variants, copy number changes and structural variants. According to the student's interest, the project can develop into more methodological or more applied follow-up work.

Preferred skills - The successful applicant will have/or expect to gain a degree in a quantitative field like mathematics, computer science or statistics. They will have a detailed knowledge of statistics and algorithms and have experience in using R and Python. They should also have a strong desire to learn cancer biology. The research atmosphere in CRUK CI is highly collaborative and so excellent communication skills are key.

Funding - This studentship is funded by Cancer Research UK and includes full funding for University and College fees and in addition, a stipend of £19,000 per annum, initially for 3 years, with funding for a further year possible as required.

Eligibility - No nationality restrictions apply to this Cancer Research UK funded studentship. Applications are invited from recent graduates or final year undergraduates who hold or expect to gain a first/upper second class degree (or equivalent) in a relevant subject from any recognised university worldwide.

How to apply - Please follow the instructions on the following webpage: (Reference: SW13453)


  1. Beerenwinkel et al (2014) Cancer evolution: mathematical models and computational inference, Systematic Biology.
  2. Ross and Markowetz (2016), OncoNEM: Inferring tumour evolution from single-cell sequencing data, Genome Biology, 17:69
  3. Schwarz et al (2015), Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction, PLoS Med, 12(2)
  4. Yuan et al (2015), BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies, Genome Biology, 16:36



Please read the Guide to Applicants before applying.

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