Research Fellow Data Science/ Medical Statistics

University of Leeds
February 26, 2024
Offerd Salary:£37,099 to £44,263
Working address:N/A
Contract Type:Fixed Term (Availabl
Working Time:Full time
Working type:N/A
Ref info:N/A
Research Fellow Data Science/ Medical Statistics

Are you an ambitious and talented researcher with expertise in health data, data science, medical statistics and/or machine learning? Do you want to join a leading research centre to implement high quality health and social care research?

We are seeking to appoint a Research Fellow in Health Data Science/Medical Statistics to join our world leading team in the Academic Unit for Ageing & Stroke Research (ASR), Leeds Institute of Health Sciences, located at the Bradford Institute for Health Research. The ASR is a long-established applied health research centre with an international reputation for research related to older people and stroke. As the University of Leeds host Unit for Health Data Research UK (HDR UK) North, the ASR is recognised as a UK and international leader in Health Data Research relating to older people. Influential recent health data research work has included development and national implementation of the multi-award-winning electronic frailty index (eFI), alongside prognostic prediction modelling work, and medicines optimisation. ASR research outputs have generated major impact through direct influence on UK healthcare policy, are cited in clinical guidelines worldwide, and have led to international changes in practice for the identification and management of older people living with frailty. The successful applicant will join this established team of mixed-methods researchers.

This role is an exciting opportunity to support the development and implementation of a number of projects using routine health data. This will include a leading role in a National Institute for Health and Care Research Health and Social Care Delivery Research (NIHR HSDR) funded project to investigate inequalities in overprescribing of medicines for older people using Clinical Practice Research Datalink (CPRD) data, and an NIHR funded project to investigate stratified care for people with hypertension using routine 24-hour blood pressure measurements.

As Research Fellow in Health Data Science/Medical Statistics you will have experience of acquiring, managing and analysing large, complex health datasets, including developing data sharing agreements. You will have a BSc with a major numerate or computational component (including statistics, machine learning, data science, mathematics or a computational science) and a relevant postgraduate qualification, or equivalent experience, in a relevant area of study. You will have excellent communication and interpersonal skills and the ability to liaise effectively with a wide range of people, including across a range of international institutions

To explore the post further or for any queries you may have, please contact:

Professor Andy Clegg, Head of ASR

Tel: +44 (0)1274 383406

Email: [email protected]

Additional Information

Working at Leeds

We are a campus based community and regular interaction with campus is an expectation of all roles in line with academic and service needs and the requirements of the role. We are also open to discussing flexible working arrangements. To find out more about the benefits of working at the University and what it is like to live and work in the Leeds area visit our Working at Leeds information page.

Location: Bradford

Faculty/Service: Faculty of Medicine & Health School/Institute: Leeds Institute of Health Sciences (LIHS) Section: Academic Unit for Ageing and Stroke Research Category: Research Grade: Grade 7 Salary: £37,099 to £44,263 per annum Working Time: 100% Full time Equivalent Post Type: Full Time Contract Type: Fixed Term (Available for 24 months to complete a specific task or time limited work, including the need for specific expertise or additional resource for a project.) Release Date: Friday 26 January 2024 Closing Date: Monday 26 February 2024 Interview Date: To be confirmed Reference: MHIHS1382

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