About the role
We are seeking a highly motivated Research Associate/Fellow to join a multidisciplinary team developing advanced MRI and sensor data analytics methods to improve our understanding of placental function and reduce the risk of stillbirth. This post forms part of the internationally fundedWellcome Trust programme, which aims to develop new approaches to measure, model and predict pregnancy health using state-of-the-art imaging, sensors computational modelling and data science.
The successful candidate will play a leading role in developing and implementing computational pipelines for MRI, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi-site datasets collected across international collaborators. The role will involve working closely with MRI physicists, clinicians, computer scientists, engineers and mathematicians to ensure high-quality, reproducible research that supports the development of new biomarkers for placental health.
About the team:
You will join a highly collaborative, multidisciplinary research environment bringing together expertise in MRI physics, obstetrics, placental biology, biomedical engineering, mathematics, computer science and artificial intelligence. The team includes researchers from the University of Nottingham, Nottingham Trent University and international collaborators working together to improve the understanding of pregnancy and placental function.
About you:
- A PhD (or close to completion) in medical imaging, computer science, physics, data science or a closely related discipline.
- Experience in MRI image analysis and scientific programming, preferably using Python and modern computational libraries.
- Experience applying machine learning or artificial intelligence to biomedical data.
- Experience managing large research datasets, databases and implementing FAIR research data management principles.
- Excellent communication and collaborative skills, with the ability to work effectively within multidisciplinary and international research teams.
What we offer
- A friendly, diverse, and supportive working environment
- A hybrid working arrangement with the blended approach of home and office working each week
- Generous holiday entitlement of 30 days (or pro rata) plus standard bank holidays and five university closure days including closure between Christmas and New Year.
- Our reward scheme grants bonuses of numerous values for excellent work
- We are committed to staff development through the provision of training, continued support, and career progression opportunities
- You will have access to a range of benefits and rewards, including fitness and health facilities, staff discounts, travel schemes and many more. To find out more about what we can offer you, follow the link below to our benefits website
This role is full time (36.25 hours), fixed term for 1 year. Further information is available in the role profile. To apply for this vacancy please click ‘Apply Now’ to complete your details.
Requests for secondment from internal candidates may be considered on the basis that prior agreement has been sought from both your current line manager and the manager of your substantive post, if you are already undertaking a secondment role.
Please contact Grazziela Figueredo, if you have further questions about this role. Please note that applications sent directly to this email address will not be accepted.
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