12320 - Research Fellow in Health Data Science
| Posting date: | 10 April 2025 |
|---|---|
| Salary: | £49,559 to £60,907 per year |
| Hours: | Full time |
| Closing date: | 08 May 2025 |
| Location: | Edinburgh, Scotland |
| Remote working: | Hybrid - work remotely up to 4 days per week |
| Company: | University of Edinburgh |
| Job type: | Temporary |
| Job reference: | 12320 |
Summary
Grade UE08: £49,559 - £60,907 per annum, pro-rata if part-time
CMVM/Institute of Neuroscience and Cardiovascular Research
Full-time: 35 hours per week
Fixed-term: for 2 years with potential to extend to September 2029
The Opportunity:
The BHF Centre of Research Excellence in Edinburgh is funded by a five-year award from the British Heart Foundation to accelerate our science. By collaborating across disciplines and institutions, we will deliver bold discoveries and foster the next generation of research leaders. We now have a suite of new opportunities in our Centre, and are seeking people with the ideas and ambition to drive forward our work.
We seek a research fellow to work with a multi-disciplinary research team in the delivery of the BHF REA Data-Driven Innovation theme, which is led by Professor Nicholas Mills (British Heart Foundation Chair of Cardiology) and Dr Atul Anand (Reader in Health Data Research). The post-holder will be responsible for conducting and supporting original research, focussing on the use of health data for the development of clinical decision-support tools in acute cardiovascular care using statistical modelling and machine learning techniques.
This post is full-time (35 hours per week); however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
The salary for this post is £49,559 - £60,907per annum.
Your skills and attributes for success:
The ability to use mainstream data science programming languages (such as R, Java, Python) for data extraction, transferring and transforming data, and statistical data analysis.
Experience in using one or more statistical software packages (e.g. R, SAS, STATA or similar).
A track record of publications in internationally leading journals and conferences.
Experience working with routine healthcare data.
CMVM/Institute of Neuroscience and Cardiovascular Research
Full-time: 35 hours per week
Fixed-term: for 2 years with potential to extend to September 2029
The Opportunity:
The BHF Centre of Research Excellence in Edinburgh is funded by a five-year award from the British Heart Foundation to accelerate our science. By collaborating across disciplines and institutions, we will deliver bold discoveries and foster the next generation of research leaders. We now have a suite of new opportunities in our Centre, and are seeking people with the ideas and ambition to drive forward our work.
We seek a research fellow to work with a multi-disciplinary research team in the delivery of the BHF REA Data-Driven Innovation theme, which is led by Professor Nicholas Mills (British Heart Foundation Chair of Cardiology) and Dr Atul Anand (Reader in Health Data Research). The post-holder will be responsible for conducting and supporting original research, focussing on the use of health data for the development of clinical decision-support tools in acute cardiovascular care using statistical modelling and machine learning techniques.
This post is full-time (35 hours per week); however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
The salary for this post is £49,559 - £60,907per annum.
Your skills and attributes for success:
The ability to use mainstream data science programming languages (such as R, Java, Python) for data extraction, transferring and transforming data, and statistical data analysis.
Experience in using one or more statistical software packages (e.g. R, SAS, STATA or similar).
A track record of publications in internationally leading journals and conferences.
Experience working with routine healthcare data.