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Research Associate – Machine learner/Data Scientist for Disease Phenotype Discovery at University College London

“From electronic health records to subphenotypes: big data to describe the overlap between heart failure, atrial fibrillation and acute coronary syndromes”

We are seeking an ambitious data scientist to join a team of researchers at the UCL Institute of Health Informatics and Health Data Research UK (HDR UK) who are investigating subphenotypes of cardiovascular diseases using electronic health records and “big data resources” including genomics. The successful candidate will serve in a leading role within an innovative and exciting environment, including: (i) Health Data Research UK, which has successfully brought together a partnership of all 5 of London’s major universities, (ii) the largest Biomedical Research Centre in the UK across UCL and UCL Hospital NHS Trust; and (iii) establishment of the Clinical Research Informatics Unit – UCLH becoming a research hospital, with a new electronic health record and research data warehouse.

Launched in February 2018, HDR UK is the new national institute for data science for health. HDR UK has four priority areas: actionable analytics including EHR phenotyping and AI, multi-omics and precision medicine, randomised trials and public health. The London Site of HDR UK comprises UCL (co-ordinating), Imperial, King’s College London, London School of Hygiene and Tropical Medicine and Queen Mary University London with their associated 8 NIHR Biomedical Research Centres. The National Director of HDR UK is Professor Andrew Morris and the London Director is Professor Harry Hemingway.

Heart failure, atrial fibrillation and acute coronary syndromes represent the greatest cardiovascular disease burden nationally and globally. They are frequently risk factors for each other, which worsens outcomes when they co-exist. Improved characterisation of the “overlap” between these diseases is required for: (i) disease definition; (ii) more accurate epidemiology regarding incidence and outcome; (iii) sub-phenotyping to inform future intervention trials; (iv) planning of health service provision for the individual diseases.

The post holder will have access to electronic health record (EHR) data from the UK (e.g. CALIBER and THIN databases) and European countries (via the BigData@Heart consortium, https://www.bigdata-heart.eu/ ). In addition, a range of multi-omic and large scale genomic datasets are available including UCLEB, BigData@Heart , UK Biobank, and the 100, 000 Genomes Project through existing collaborations with other disease-specific and biomarker consortia. Building on the reputation of the UCL Institute of Health Informatics as a leader in EHR phenotype discovery and harmonisation, there is increased emphasis on development of new subphenotypes using new data (e.g. -omics) and new methods (e.g. machine learning).

The post holder will be based at the UCL Institute of Health Informatics, reporting to Dr Amitava Banerjee, Associate Professor in Clinical Data Science.

The post holder will be required to work 100% FTE. The appointment is available immediately and is funded until 28 February 2021.

Suitable candidates will have a background in data science, genetic epidemiology or bioinformatics; Experience in machine learning is essential. Experience with electronic health records and healthcare data are desirable but not essential.

UCL vacancy reference: 1720567        

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 Dr Ami Banerjee ( a.banerjee@ucl.ac.uk ) for an informal discussion. If you have any queries regarding the application process, please contact Nadia Jackson ( nadia.jackson@ucl.ac.uk ).

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

Interview Date: TBC

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