Postdoctoral Research Associate in Machine Learning for Stroke
Advert Reference Number: 703
Job Location: Milton Keynes, Remote/Hybrid
Department: School of Physical Sciences
Salary: £38,249 to £40,497
Closing Date: Wednesday 9th April 2025
Weekly Working Hours: 37
Contract Type: Fixed Term Contract
Fixed Term Contract End Date: 31st July 2025
Welsh Language: Not Applicable
About the Role
The role holder will perform research into the use of machine learning informed by the results of biophysical simulations in the prediction and analysis of ischemic stroke.
The majority of strokes are ischemic (caused by a blockage in blood vessels supplying the brain) and some of the most severe strokes are caused by emboli (blood clots and plaques that move into the cerebral vasculature to block the blood vessels). Embolization can come from a range of sources, and understanding its effects may help to identify clinical interventions to improve patient outcomes. Our goal is to understand and predict the patterns of embolization that cause specific strokes both from modelling and analysing medical images.
The project will involve using machine learning (deep learning / convolutional neural nets) to develop state of the art techniques to understand how strokes with origins from different embolus types can be identified in imaging, building on data from Monte Carlo simulation techniques (Hague, JP et al. Scientific Reports, 13 (2023) 3021). Programming skills with Python are essential, as are familiarity with machine learning techniques. You should be willing to apply your skills to, and undertake training on, biomedical systems.
This project offers:
1. The chance to contribute to research papers.
2. Opportunities to use and develop state of the art data analysis tools.
3. Opportunities to develop skills in machine learning.
Key Responsibilities
1. To develop a deep-learning model to generate simulated images of embolic strokes.
2. To document this model.
3. To contribute to writing papers and give presentations (at e.g. conferences and at the OU) as required.
4. To contribute to the lively academic environment in the School of Physical Sciences.
Skills and Experience
Essential:
1. A PhD in physics or a closely related field (PhD candidates who have submitted their thesis will be considered).
2. Skilled programmer in Python.
3. Experience with deep-learning techniques (including a variety of architectures such as convolutional neural networks, transformers and stable diffusion models).
4. Ability to communicate research results effectively as demonstrated by a record of peer-reviewed publications and conference submissions commensurate with career stage.
5. Good oral and written communication skills.
6. Can demonstrate being a good team worker and able to work under own initiative.
7. Willingness to work on biological systems and undertake training in this area if required.
8. A strong record of research and/or knowledge exchange that is commensurate to the position.
Desirable:
1. Relevant knowledge of biological and physiological systems.
Essential Requirements
To apply for this role please submit the following:
CV and supporting statement, up to 1,000 words, you should set out in your supporting statement why you’re interested in this role and provide examples of where your skills and experience meet the essential and desirable criteria for this role as detailed above.
Work location
It is anticipated that a hybrid working pattern can be adopted for this role, where you can work from home and the office. However, as this role is contractually aligned to our Milton Keynes office it is expected that you will attend the office at least three days per week, and in response to business needs.
Flexible working
We are open to discussions about flexible working. Whether it’s a job share, part time, compressed hours or another working arrangement. Please reach out to us to discuss what may work for you and the role.
Early closing date notification
We may close this job advert earlier than the published closing date where a satisfactory number of applications are received. We would therefore encourage early applications.
Contact us
If you have any queries or questions about the recruitment process, or regarding your application, please contact: Careers@open.ac.uk.
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