Research Fellow in Health Data Sciences

Health Sciences: Research

Location: Guildford
Salary: £34,308 to £38,592 per annum
Fixed Term
Post Type: Full Time
Closing Date: 23.59 hours GMT on Monday 06 February 2023
Reference: 001923

This is a post-doctoral opportunity to participate in publicly-funded international collaborations on data-centred research in the area of healthy ageing and dementia. The post holder will contribute to two research projects. The Harmonized Diagnostic Assessment of Dementia (DAD) for Longitudinal Ageing Study in India (LASI) is a large population-based study supported by the US National Institute on Aging, a renowned research funder in the area, to understand the risks and prevalence of dementia in India. LASI-DAD implements the Harmonized Cognitive Assessment Protocol (HCAP, https://hrs.isr.umich.edu/data-products/hcap) and is conducted within a larger longitudinal ageing study in India with a nationally representative of over 70,000 middle- and older-aged adults. The second project in which the post holder will be involved is also funded by the US National Institute on Aging, aiming to develop early markers of cognitive decline and dementia from survey response behaviours. The project will utilise data from large longitudinal ageing studies like the LASI, the English Longitudinal Ageing Study (ELSA, https://www.elsa-project.ac.uk/), the Survey of Health, Ageing and Retirement in Europe (SHARE, http://www.share-project.org/), and the Health and Retirement Study (HRS, https://hrs.isr.umich.edu/). The two projects aspire to understand and predict risks of mild cognitive impairment (MCI) and dementia using population-based longitudinal ageing data from various aspects. That is, the first project (LASI-DAD) mainly focuses on survey-based cognitive, health, and quality of life assessment data; meanwhile, the second project focuses on the processes people provide those data (i.e., the survey response behaviours).

The post holder will develop and test data science and statistical learning models that use the aforementioned longitudinal ageing data to predict MCI and dementia and to better stratify individuals according to their risk profiles. The post holder will lead as well as co-authoring high-quality and high-impact publications, and explore new opportunities for knowledge discovery and public health and clinical applications. An ideal post holder will have a PhD or equivalent degree in health and biomedical informatics, data sciences, biostatistics, psychometrics/econometrics, public health, epidemiology, or related fields. Outstanding analytical skills with experiences in statistical programming (R or Python) and relevant experiences in longitudinal research on cognitive decline and healthy ageing are strongly desired.

Interested applicants are invited to submit an application consisting of:

1. a detailed CV,
2. a 2-page research statement summarising research achievements and future plan, and
3. contact for 2-3 referees.

This is a 20-month full-time post with the possibility of extending to 26-32 months, subject to availability of future funding. Please direct inquiries to Dr Haomiao Jin, Lecturer in Health Data Sciences (h.jin@surrey.ac.uk).

Further details: Job Description

For more information and to apply online, please download the further details and click on the 'apply online' button above.

In return we offer a generous pension, relocation assistance where appropriate , flexible working options including job share and blended home/campus working locations (dependent on work duties), access to world-class leisure facilities on campus, a range of travel schemes and supportive family friendly benefits including an excellent on-site nursery.

Click here to find out more about the benefits we offer to support you.

The University of Surrey is committed to providing an inclusive environment that offers equal opportunities for all. We place great value on diversity and are seeking to increase the diversity within our community. Therefore we particularly encourage applications from under-represented groups, such as people from Black, Asian and minority ethnic groups and people with disabilities.