Research Officer - Statistician
| Dyddiad hysbysebu: | 09 Rhagfyr 2025 |
|---|---|
| Cyflog: | £39,355 i £45,413 bob blwyddyn |
| Gwybodaeth ychwanegol am y cyflog: | together with USS pension benefits |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 04 Ionawr 2026 |
| Lleoliad: | Swansea, Wales |
| Gweithio o bell: | Ar y safle yn unig |
| Cwmni: | Swansea University |
| Math o swydd: | Cytundeb |
| Cyfeirnod swydd: | SU01344 |
Crynodeb
This is a Fixed Term role until September 2027 working full-time.
The successful candidate will play a central role in a Diabetes UK funded research programme at Swansea University. The project’s overarching aim is to understand the development of diabetic peripheral neuropathy (DPN) and its relationship with longitudinal risk factors in people with type 2 diabetes, using electronic health records in trusted research environments. You will lead statistical modelling of trajectories for HbA1c, systolic blood pressure, BMI, total cholesterol and triglycerides, employing linear-spline mixed-effects models with measurement occasions nested within individuals. Your modelling will accommodate changing scale and variance over time, differing numbers of measurements per individual, and a missing-at-random assumption.
You will then integrate these epidemiological analyses with artificial-intelligence derived retinal image biomarkers and risk-prediction models, to address four key research questions: identifying risk-factor patterns that precede DPN onset; mapping how AI-based retinal risk prediction aligns with longitudinal risk trajectories; exploring how DPN development links with other diabetes complications; and assessing how newer medications (e.g., GLP-1 RA and GLP-1 RA+GIP analogues) may influence DPN risk. You will prepare descriptive analyses, fit multiple cox regression models with confounder adjustment, conduct stratified and subgroup analyses (including by sex, ethnicity, diabetes type and hypoglycaemia history), and produce visual outputs such as heat-maps of retinal features and risk-trajectory associations. You will collaborate with clinical, data-science and informatics colleagues, contribute to manuscripts and reports, and help translate findings into clinical and policy-relevant messages. The post offers the chance to engage with state-of-the-art methodology, advance your publication profile and contribute to real-world impact in diabetes care.
The successful candidate will play a central role in a Diabetes UK funded research programme at Swansea University. The project’s overarching aim is to understand the development of diabetic peripheral neuropathy (DPN) and its relationship with longitudinal risk factors in people with type 2 diabetes, using electronic health records in trusted research environments. You will lead statistical modelling of trajectories for HbA1c, systolic blood pressure, BMI, total cholesterol and triglycerides, employing linear-spline mixed-effects models with measurement occasions nested within individuals. Your modelling will accommodate changing scale and variance over time, differing numbers of measurements per individual, and a missing-at-random assumption.
You will then integrate these epidemiological analyses with artificial-intelligence derived retinal image biomarkers and risk-prediction models, to address four key research questions: identifying risk-factor patterns that precede DPN onset; mapping how AI-based retinal risk prediction aligns with longitudinal risk trajectories; exploring how DPN development links with other diabetes complications; and assessing how newer medications (e.g., GLP-1 RA and GLP-1 RA+GIP analogues) may influence DPN risk. You will prepare descriptive analyses, fit multiple cox regression models with confounder adjustment, conduct stratified and subgroup analyses (including by sex, ethnicity, diabetes type and hypoglycaemia history), and produce visual outputs such as heat-maps of retinal features and risk-trajectory associations. You will collaborate with clinical, data-science and informatics colleagues, contribute to manuscripts and reports, and help translate findings into clinical and policy-relevant messages. The post offers the chance to engage with state-of-the-art methodology, advance your publication profile and contribute to real-world impact in diabetes care.