Postdoctoral Researcher in Statistical Machine Learning

Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Old Road Campus, Oxford

Applications to be received by 12pm on Wednesday 26th of February 2020

Grade 7: £32,817 - £40,322 p.a.

We are seeking to appoint a Postdoctoral Researcher in Statistical Machine Learning who will based at the Big Data Institute and be part of the Oxford analysis team that provides biostatistical expertise for Oxford-Novartis Collaboration for AI in Medicine. The postholder will apply and develop state of the art statistical machine learning algorithms for characterisation of early progressive and disease continuum.


Under the line management of Professor Chris Holmes and direction of Professor Thomas Nichols, the primary focus will involve integrated analyses of different brain MRI modalities and clinical data for modelling disease continuum and early progression.


Whilst you will be predominantly based at the Big Data Institute, you will also be expected to spend time at the Department of Statistics.


Your responsibilities and duties will include, adapting existing and develop state of the art statistical machine learning methods disease modelling, reviewing and refining methods as appropriate and manage your own academic research and administrative activities as well as contribute ideas for new research projects. You will also develop ideas for generating research income, and present detailed research proposals to senior researchers as well as collaborate in the preparation of research publications and present papers at conferences or public meetings.


You will hold or be close to completion of a relevant PhD/DPhil in statistics, computer science, engineering or related discipline and possess sufficient specialist knowledge in the discipline to work within established research programmes.


This full-time position is fixed-term for 2 years in the first instance, with the possibility of extension.


Further particulars, including details of how to apply, can be obtained from the document below. Applications for this vacancy should be made online and you will be required to upload a CV and supporting statement as part of your application.


The closing date for this post will be 12.00 noon on Wednesday 26 February 2020.

Contact: Genevieve Moffa (01865 612892)