09 Oct
University of Leeds
Leeds
Are you an ambitious researcher looking for your next challenge? Do you have an established background in data science and interests in modelling the determinants of health? Do you want to further your career in one of the UK’s leading research-intensive universities?
Policy Modelling for Health is a thematic pillar of the £35 million Population Health Improvement UK (PHI-UK) network, funded by UK Research and Innovation (UKRI), bringing together expertise and insight from across research, public health and community organisations. Its aim is to find innovative and inclusive ways to improve the health of people,
places and communities and reduce health inequalities through the development and evaluation of long-lasting and environmentally sustainable interventions. Policy Modelling for Health comprises experts from six universities, local and national government, agencies, charities and citizen's groups who will develop computer models to show how tax, welfare, pensions and inheritance policies might affect health inequality outcomes to help policymakers understand their impacts on people in their area. It will incorporate wide-ranging insights into these models to make sure they answer the most pressing questions, inform real world decisions, and are relevant and inclusive across different groups in society. By doing so we will address the economic determinants of health and health inequalities through supporting the development and implementation of high-impact, established and innovative population-level policies using complex systems approaches to policy modelling. Policy Modelling for Health leverages insights and methods developed as part of prior major investments,
including the Systems Science in Public Health and Health Economics Research (SIPHER) consortium.
You will lead on the maintenance and expansion of a dynamic microsimulation model code base, which you will use to develop policy models for population health. In addition, you will collaborate on the development of synthetic population datasets for use in dynamic policy models, with responsibility for developing metrics and indices from these data which reveal spatial and sub group health inequalities. You will collaborate widely with academic and other partners to shape research questions, disseminate findings and contribute to the evidence base informing health policy.
You will have a PhD in data science, computer science, health informatics, maths, engineering or a related highly numerate discipline,
together with a strong background in applied data science. You will ideally have experience of modelling in a health context. You will have a positive attitude to collaborative research and the drive to make a significant contribution to making this ground-breaking project a success.
Please note that this post may be suitable for sponsorship under the Skilled Worker visa route but first-time applicants might need to qualify for salary concessions. For more information please visit: www.gov.uk/skilled-worker-visa.
For research and academic posts, we will consider eligibility under the Global Talent visa. For more information please visit: https://www.gov.uk/global-talent
What we offer in return
• 26 days holiday plus approx.16 Bank Holidays/days that the University is closed by custom (including Christmas) – That’s 42 days a year!
• Generous pension scheme plus life assurance– the University contributes 14.5% of salary
• Health and Wellbeing: Discounted staff membership options at The Edge, our state-of-the-art Campus gym, with a pool, sauna, climbing wall, cycle circuit, and sports halls.
• Personal Development: Access to courses run by our Organisational Development & Professional Learning team.
• Access to on-site childcare, shopping discounts and travel schemes are also available.
And much more!
To explore the post further or for any queries you may have, please contact:
Nik Lomax, Professor of Population Geography
Tel: +44 (0)113 343 3321
Email: [email protected]
▶️ Research Fellow in Dynamic Microsimulation for Health
🖊️ University of Leeds
📍 Leeds