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9745- Research Fellow in Computational Cardiology
| Posting date: | 22 August 2024 |
|---|---|
| Salary: | £39,347 to £46,974 per year |
| Hours: | Full time |
| Closing date: | 05 September 2024 |
| Location: | Edinburgh, Scotland |
| Remote working: | Hybrid - work remotely up to 3 days per week |
| Company: | University of Edinburgh |
| Job type: | Temporary |
| Job reference: | 9745 |
Summary
Grade UE07- £39,347- £46,974
College of Medicine and Veterinary Medicine- Deanery of clinical Sciences/ Centre for Cardiovascular Science
Full-time- 35 hours per week
Fixed-term- 12 months
The Opportunity:
Applications are invited for a Post Doctoral Research Associate to work on an exciting project developing and evaluating digital twin models for the British Heart Foundation-funded clinical study “Realistic Computational Electrophysiology Simulations for the Targeted Treatment of Atrial Fibrillation” (ReCETT-AF).” The post holder will work closely with the existing study team, building the infrastructure to support large-scale clinical trials using the digital twinning approach developed for ReCETT-AF.
This position offers an opportunity to work closely with Dr Steven Williams (the project’s Principal Investigator) as part of the Edinburgh Computational Cardiovascular Imaging (ECCI) research group at the Centre for Cardiovascular Science, University of Edinburgh.
The post holder will be based at the University of Edinburgh’s Centre for Cardiovascular Science, a leading center combining world-leading cardiovascular disease research, state-of-the-art machine learning and health data science research and a strong interdisciplinary research culture.
This post is a 12 month fixed-term, 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.
This post is available until the 31st October 2025.
Your skills and attributes for success:
-Excellence in Python software development for scientific computing
-A strong foundation, understanding or willingness to learn computational methods for digital twinning
-Prior experience handling large quantities of clinical or non-clinical real world data
-Prior experience in high performance computing
-Relevant domain knowledge relating to cardiac electrophysiology
College of Medicine and Veterinary Medicine- Deanery of clinical Sciences/ Centre for Cardiovascular Science
Full-time- 35 hours per week
Fixed-term- 12 months
The Opportunity:
Applications are invited for a Post Doctoral Research Associate to work on an exciting project developing and evaluating digital twin models for the British Heart Foundation-funded clinical study “Realistic Computational Electrophysiology Simulations for the Targeted Treatment of Atrial Fibrillation” (ReCETT-AF).” The post holder will work closely with the existing study team, building the infrastructure to support large-scale clinical trials using the digital twinning approach developed for ReCETT-AF.
This position offers an opportunity to work closely with Dr Steven Williams (the project’s Principal Investigator) as part of the Edinburgh Computational Cardiovascular Imaging (ECCI) research group at the Centre for Cardiovascular Science, University of Edinburgh.
The post holder will be based at the University of Edinburgh’s Centre for Cardiovascular Science, a leading center combining world-leading cardiovascular disease research, state-of-the-art machine learning and health data science research and a strong interdisciplinary research culture.
This post is a 12 month fixed-term, 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.
This post is available until the 31st October 2025.
Your skills and attributes for success:
-Excellence in Python software development for scientific computing
-A strong foundation, understanding or willingness to learn computational methods for digital twinning
-Prior experience handling large quantities of clinical or non-clinical real world data
-Prior experience in high performance computing
-Relevant domain knowledge relating to cardiac electrophysiology