12695 - Research Assistant/Research Associate
| Dyddiad hysbysebu: | 23 Mehefin 2025 |
|---|---|
| Cyflog: | £34,132 i £48,149 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 21 Gorffennaf 2025 |
| Lleoliad: | Edinburgh, Scotland |
| Gweithio o bell: | Hybrid - gweithio o bell hyd at 4 ddiwrnod yr wythnos |
| Cwmni: | University of Edinburgh |
| Math o swydd: | Dros dro |
| Cyfeirnod swydd: | 12695 |
Crynodeb
Grade UE06: £34,132 to £39,355 or UE07: £40,497 to £48,149 per annum, pro-rata if part time
College of Science and Engineering / School of Informatics
Full-time: 35 hours per week
Fixed term: to 31 October 2026
The School of Informatics, University of Edinburgh invites applications for two posts, which can be appointed at either Research Assistant or Research Associate levels, to contribute to cutting-edge research in machine learning for time series health and care data with opportunities for innovation, interdisciplinary collaboration, and career development.
The Opportunity:
The MoveR Laboratory at the University of Edinburgh (mover.ed.ac.uk) is a multidisciplinary research hub focused on improving movement, rehabilitation, and physical health across the life course. Combining expertise in engineering, informatics, medicine, and social science, MoveR develops low-cost, people-centred digital technologies that address real-world needs in health and care.
At its core is the MoveR Wearable, a bespoke, affordable sensor platform that captures rich movement, physiological, and behavioural data, supporting on-device machine learning and remote monitoring. The lab explores digital biomarkers and predictive analytics to support conditions such as Parkinson’s, stroke, cerebral palsy, and post-operative recovery. Research areas span biomechanical modelling, physical activity analysis, and digital phenotyping using edge computing.
MoveR collaborates extensively with industry, healthcare providers, and the public, translating research into products and services through partnerships and innovation funding. The team, led by Prof. Kia Nazarpour, includes researchers with expertise in AI, sensor systems, co-design, and translational health tech.
We are currently recruiting for two full-time positions at Grades UE06 (Research Assistant) or Grade UE07 (Research Associate) to join our team. These posts are 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.
Your skills and attributes for success:
For the UE06 Research Assistant position:
An MSc (or equivalent) in broad areas of informatics, engineering, physics, or other natural, biological, or medical sciences or equivalent work experience
Background in “time-series analysis and machine learning” or “Machine learning in electronic hardware including circuit design and fabrication”
Strong programming skills, ideally including Python
Strong presentation skills
Proactive approach to building partnerships
Experience of writing proposals for funding/investment
For the UE07 Research Associate position:
A PhD in broad areas of informatics, engineering, physics, or other natural, biological, or medical sciences or equivalent work experience
Background in “time-series analysis and machine learning” or “Machine learning in electronic hardware including circuit design and fabrication”
Full understanding and experience of co-creation, co-design in digital health and care
Track record of innovative (beyond ordinary) research work in the higher education, health, care sectors, evidenced by publications, e.g. patents
Strong presentation skills
Proactive approach to building strategic partnerships with stakeholders
Experience of writing proposals for funding/investment
College of Science and Engineering / School of Informatics
Full-time: 35 hours per week
Fixed term: to 31 October 2026
The School of Informatics, University of Edinburgh invites applications for two posts, which can be appointed at either Research Assistant or Research Associate levels, to contribute to cutting-edge research in machine learning for time series health and care data with opportunities for innovation, interdisciplinary collaboration, and career development.
The Opportunity:
The MoveR Laboratory at the University of Edinburgh (mover.ed.ac.uk) is a multidisciplinary research hub focused on improving movement, rehabilitation, and physical health across the life course. Combining expertise in engineering, informatics, medicine, and social science, MoveR develops low-cost, people-centred digital technologies that address real-world needs in health and care.
At its core is the MoveR Wearable, a bespoke, affordable sensor platform that captures rich movement, physiological, and behavioural data, supporting on-device machine learning and remote monitoring. The lab explores digital biomarkers and predictive analytics to support conditions such as Parkinson’s, stroke, cerebral palsy, and post-operative recovery. Research areas span biomechanical modelling, physical activity analysis, and digital phenotyping using edge computing.
MoveR collaborates extensively with industry, healthcare providers, and the public, translating research into products and services through partnerships and innovation funding. The team, led by Prof. Kia Nazarpour, includes researchers with expertise in AI, sensor systems, co-design, and translational health tech.
We are currently recruiting for two full-time positions at Grades UE06 (Research Assistant) or Grade UE07 (Research Associate) to join our team. These posts are 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.
Your skills and attributes for success:
For the UE06 Research Assistant position:
An MSc (or equivalent) in broad areas of informatics, engineering, physics, or other natural, biological, or medical sciences or equivalent work experience
Background in “time-series analysis and machine learning” or “Machine learning in electronic hardware including circuit design and fabrication”
Strong programming skills, ideally including Python
Strong presentation skills
Proactive approach to building partnerships
Experience of writing proposals for funding/investment
For the UE07 Research Associate position:
A PhD in broad areas of informatics, engineering, physics, or other natural, biological, or medical sciences or equivalent work experience
Background in “time-series analysis and machine learning” or “Machine learning in electronic hardware including circuit design and fabrication”
Full understanding and experience of co-creation, co-design in digital health and care
Track record of innovative (beyond ordinary) research work in the higher education, health, care sectors, evidenced by publications, e.g. patents
Strong presentation skills
Proactive approach to building strategic partnerships with stakeholders
Experience of writing proposals for funding/investment