12688 - Research Fellow (Inequalities in Ageing-in-Place)
| Dyddiad hysbysebu: | 24 Mehefin 2025 |
|---|---|
| Cyflog: | £40,497 i £48,148 bob blwyddyn, pro rata |
| Oriau: | Rhan Amser |
| Dyddiad cau: | 08 Gorffennaf 2025 |
| Lleoliad: | Edinburgh, Scotland |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 1 diwrnod yr wythnos |
| Cwmni: | University of Edinburgh |
| Math o swydd: | Dros dro |
| Cyfeirnod swydd: | 12688 |
Crynodeb
Grade UE07: £40,497 to £48,148 per annum, pro-rata
CAHSS / School of Social and Political Science
Part-time: 7 hours per week
Fixed term: to end no later than 30 June 2026
We are looking for a Research Fellow with experience of quantitative modelling of social phenomena across the lifecourse to join the NIHR-funded project “Interdisciplinary approaches to understanding inequalities in ageing-in-place for people with multiple long-term conditions”.
The Opportunity:
You will conduct quantitative modelling of social phenomena across the lifecourse exploring inequalities in ageing-in-place for people with multiple long-term conditions to examine how social categories are constructed and lead to social inequalities that are culturally specific and vary across time and space.
You will also co-ordinate the activities of the Edinburgh-based collaborators to build an interdisciplinary team participating in a programme of training and in ‘data sprints’ to explore the implications of ageing-in-place and its intersections with health inequalities.
This post is fixed term from July 2025 to no later than 30 June 2026.
This post is part time (7 hours per week). We are open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote within the UK and regular on-campus working.
The salary for this post is £40,497 to £48,148 per annum pro rata.
Your skills and attributes for success:
A PhD (completed or close to completion) in Social Statistics. Or equivalent experience of leading quantitative Social Science research.
Experience of quantitative modelling of social phenomena across the lifecourse.
Excellent statistical knowledge (including the use of statistical packages such as Stata and/or R) and demonstrable competency in the analysis of complex social survey data which includes stratification and clustering in its sample design.
Experience of Generalised Linear Models, Multi-level modelling and Longitudinal data analysis models.
Experience of research project co-ordination, across disciplinary boundaries and institutions.
CAHSS / School of Social and Political Science
Part-time: 7 hours per week
Fixed term: to end no later than 30 June 2026
We are looking for a Research Fellow with experience of quantitative modelling of social phenomena across the lifecourse to join the NIHR-funded project “Interdisciplinary approaches to understanding inequalities in ageing-in-place for people with multiple long-term conditions”.
The Opportunity:
You will conduct quantitative modelling of social phenomena across the lifecourse exploring inequalities in ageing-in-place for people with multiple long-term conditions to examine how social categories are constructed and lead to social inequalities that are culturally specific and vary across time and space.
You will also co-ordinate the activities of the Edinburgh-based collaborators to build an interdisciplinary team participating in a programme of training and in ‘data sprints’ to explore the implications of ageing-in-place and its intersections with health inequalities.
This post is fixed term from July 2025 to no later than 30 June 2026.
This post is part time (7 hours per week). We are open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote within the UK and regular on-campus working.
The salary for this post is £40,497 to £48,148 per annum pro rata.
Your skills and attributes for success:
A PhD (completed or close to completion) in Social Statistics. Or equivalent experience of leading quantitative Social Science research.
Experience of quantitative modelling of social phenomena across the lifecourse.
Excellent statistical knowledge (including the use of statistical packages such as Stata and/or R) and demonstrable competency in the analysis of complex social survey data which includes stratification and clustering in its sample design.
Experience of Generalised Linear Models, Multi-level modelling and Longitudinal data analysis models.
Experience of research project co-ordination, across disciplinary boundaries and institutions.