Centre for Human Genetics, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Roosevelt Drive, Headington, Oxford, OX3 7BN
We are seeking to appoint a Postdoctoral Research Assistant to join the University of Oxford's Big Data Institute, to work on efficient simulation methods and analytical models of background selection.
Background selection is one of the most important evolutionary forces, yet we lack efficient simulation and analysis methods. You will work in the Kelleher group, in close collaboration with the Lohse group in Edinburgh to develop new simulation and inference methods, and implement these in the msprime simulator and/or tskit library. You will develop high-quality open-source software as part of the globally-distributed tskit-dev team.
It is essential that you hold a PhD/ DPhil (or close to completion) in a quantitative science subject, together with experience in mathematical population genetics, in particular coalescent processes, and strong Python programming skills. You will have experience of developing population genetic simulation and/or inference methods, and excellent communication skills, including the ability to write for publication, present research proposals and results, and represent the research group at meetings.
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 the selection criteria for the post using examples of your skills and experience.
This position is offered full time on a fixed term contract until 30 June 2026 and is funded by the EPSRC.
Only applications received before 12 midday on Wednesday 12 June 2024 will be considered. Please quote 172597 on all correspondence.
Contact Person : Margarita Monroy Vacancy ID : 172597 Contact Phone : 01865287757 Closing Date & Time : 12-Jun-2024 12:00 Pay Scale : STANDARD GRADE 7 Contact Email : [email protected] Salary (£) : Salary in range £36,024 - £40,521 per annum