See http://www.jobs.cam.ac.uk/job/7372/ for full details
SOUND is an international research project funded by the European Commission (EC) within its Horizon 2020 Research and Innovation programme "Personalizing Health and Care". Its objective is to create the bioinformatic tools for statistically informed use of personal genomic and other `omic data in medicine, including cancers and rare metabolic diseases. It comprises partners from top research institutions including Cambridge, Munich, Zurich, Seattle, Heidelberg and Lisbon, and will run from September 2015 for 3 years.
Our contribution to SOUND consists largely of two aspects: 1) The development of methods for liquid biopsy data, and 2) Methods for robust benchmarking of genomics experiments/methods. In the first we will collaborate with the Brenton and Fitzgerald clinical labs to obtain relevant motivational data sets for monitoring circulating DNA in cancer patients. We will create computational workflows to facilitate robust and reproducible research, developing new methods to address shortcomings that we identify in existing approaches during this exercise. The second project will seek to establish standards for the reporting of benchmarking studies when describing new methods, while also considering the design of experiments for verifying mutation calls from genome sequencing projects.
The SOUND team in the Statistics and Computational Biology lab will consist of a Research Associate and a PhD student led by Professor Simon Tavaré FRS and Dr Andy Lynch. They will interact with the other members of the lab and the wider computational biology and cancer research communities within the Cancer Research UK Cambridge Institute. Further to this there will be interactions with the other members of the consortium and opportunities to spend time at those sites.
The focus of the Statistics and Computational Biology Lab is the development and application of principled statistical analysis methods for understanding cancer, from early stage disease, progression, response to treatment and relapse. Our research is therefore a mixture of methodology, for example for next-generation sequencing, and data analysis of clinically relevant data sets. These tools are made available through the public software environments R and Bioconductor, thereby supporting our focus on reproducible research.
The successful candidate will have a PhD in relevant discipline, a background in statistics/mathematics with experience of genomics analysis. Strong programming skills are essential, with experienced R programmers being particularly advantaged. Experience of cancer research would also be advantageous.
