RA / PhD Student

A Research Assistant/PhD Student position is available from 1st January 2027 for up to 36 months, in the Bivalve Transmissible Neoplasia Group at the Department of Zoology, University of Cambridge. This Research Assistant position would allow the post holder to undertake a postgraduate course (PhD) at the University in the Department of Zoology, located in Central Cambridge. The RA/PhD Student must have a strong background in computational and mathematical biology, and will work on a project to explore the genetic diversity and long-term somatic evolution of bivalve transmissible cancers. They will be part of a team of researchers with complementary expertise in bioinformatics, cell biology and evolutionary biology, with opportunities for collaboration across the project while also pursuing their own objectives. They will have a dedicated training budget and a supportive and engaged supervision team. This work will be undertaken as part of a grant on the genetics and evolution of marine transmissible cancers, and will directly contribute towards the Doctor of Philosophy (PhD) studies that the successful candidate will be enrolled in at the Department of Zoology, University of Cambridge. Duties will include:

• Development of species-specific protocols
• Data collection, analysis and interpretation
• Preparation of manuscripts for publication
• Continuing to update knowledge and develop skills
• Co-supervision of project students working on related topics
• Collaboration with team members
  
  Strong background in computational and mathematical biology
• Demonstrable proficiency in scientific coding in R, Python, Java and Bash, including manipulation of large biological datasets
• Demonstrable experience in computational analysis of genetic variant datasets
• Demonstrable experience in analysis of somatic mutation data, including modelling of allele fraction distributions
• Demonstrable experience in analysis of genetic recombination and linkage disequilibrium
• Demonstrable experience in phylogenetic reconstruction, including maximum likelihood inference
• Demonstrable experience in development and application of machine learning models, including convolutional neural networks
• Demonstrable knowledge of modelling methods including k-means clustering, principal component analysis and generalised linear models
• Demonstrable knowledge of the transmissible cancer field and enthusiasm for translating mechanistic insights