Liquid biopsies based on cell-free DNA promise to revolutionise cancer early detection and treatment. The Schuster-Böckler group at Ludwig Oxford in the Big Data Institute works at the forefront of this revolution, collaborating with clinical researchers who are producing large amounts of cell-free DNA sequencing data. Your job will be to drive the research, applying existing and developing new algorithms to maximise the diagnostic potential of these data. This will involve developing statistical discriminators and/or machine-learning based models to best classify samples and put that into the appropriate clinical and biological context.
To achieve this goal, you will forge and foster links with experimental and computational scientists in Oxford and beyond, and prepare your results for presentation internally as well as for publication in journals and at national and international conferences, representing the Institute locally, nationally and internationally.
It is essential that you hold a PhD with research experience in academia or industry. It is expected that you have demonstrable experience in the statistical analysis of large, high-dimensional and heterogeneous data. Excellent communication skills, both written and oral, with the ability to present to the scientific community and the lay public, along with the ability to work in a multi-disciplinary and distributed team are also required. Most importantly, you should be a passionate scientist who is curious, independent in their thinking, and willing to take a leading role in driving the project forward.
Applications for this vacancy are to be made online and you will be required to upload a supporting statement and CV as part of your online application. Your supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience.
This position is offered full time on a fixed term contract for 24 months and is funded by an MRC Grant and a Ludwig Grant.
Applications must be made online by 12.00 noon (UK time) on Tuesday 7 June 2022