We are seeking to expand our team of scientists working at the interface of phenomic information from electronic health record data and genomic information. Broadly we seek to add value to and exploit a wide range of genomic-electronic health record resources including the 100,000 genomes project and UK Biobank. Our team at UCL plays leading roles in Health Data Research UK (HDR UK), in multi-omic cohorts such as UCLEB, in Biomedical Research Centre initiatives to embed genomic information in clinical care, and in international consortia such as the Innovative Medicines Initiative Big Data@ Heart (https://www.bigdata-heart.eu/).

Scientific problem: The particular focus of this post is on better understanding the clinical significance and actionability of genomic information in the light of disease agnostic, longitudinal phenotype information from electronic health records.

Using genetic information linked to longitudinal electronic health record phenotype information, the post holder will be expected to plan and execute analyses and visualise and interpret the results for communication to clinical and scientific audiences. The post holder will work across actionable rare variants, and (potentially) actionable information from polygenic risk scores. The precise focus will be developed based on matching the scientific opportunity with the skills and experience of the post-holder.

The post holder will be required to work full time (100% FTE) and is funded until 30 November 2020 in the first instance, and suitable candidates will be supported in applications for Fellowships. You will be jointly supervised by Professor Harry Hemingway Director of HDR UK London and Director of the Institute of Health Informatics and Professor Aroon Hingorani (Professor of Genetic Epidemiology).

You will have a PhD in statistical genetics, genetic epidemiology, or bioinformatics; a relevant publication track record; and an interest in clinical applications. Experience in the analysis of sequence data, and the application of complex statistical models or statistical learning approaches would be advantageous. Experience of accessing, curating, analysing, and interpreting electronic health record data from diverse sources is an advantage, but for candidates without this expertise, in-post training will be available. The post holder will be motivated and resourceful, an effective independent worker, and a collaborative member of an international multidisciplinary team. We place a strong emphasis on supporting career development and on the potential for independence. 

UCL vacancy reference: 1736066

Applicants should apply online. To access further details about the position and how to apply please click on the ‘Apply’ button below.

Interested candidates are welcome to contact Professor Harry Hemingway (h.hemingway@ucl.ac.uk) for an informal discussion. If you have any queries regarding the application process, please contact Dr Wing-Chau Tung (w.tung@ucl.ac.uk).

Closing Date: 22 August 2018.

Latest time for the submission of applications: 23:59.

Interview Date: TBC.

UCL Taking Action for Equality.

We will consider applications to work on a part-time, flexible, and job share basis wherever possible.

Our department holds an Athena SWAN Bronze award, in recognition of our commitment to advancing gender equality.