Weekly Working Hours:37
Contract Type:Fixed Term Contract
Fixed Term Contract: End Date:30 September 2026
Welsh Language:Not Applicable
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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 cell organisation in tumoroids.
Cells are active and can both manipulate and be manipulated by their environment. This feedback between environment and cells can lead tissues to become self-organised. This self-organisation is important for the correct functioning of biological tissues. We hope to understand (and eventually control) the arrangements of cells in spheroids and tumoroids.
The project will primarily involve the development and testing of machine learning techniques based on biophysical simulations to predict the organisation of tumoroids and analyse their level of health. The successful candidate will also grow tumoroids in the laboratory and perform confocal microscopy on those tissues to establish the organisation of cells. These data will then be used to validate our state-of-the-art techniques to predict the arrangements of cells in biological tissues, building on the CONDOR simulation technique (Hague, J. P. et al. Physical Review Research, 2, 043217, 2020).
You will use our mature simulation code to carry out simulations. Knowledge of a python framework for machine learning is essential (e.g. TensorFlow), as is knowledge of the C++ programming language. Some knowledge of Linux is desirable. You should be willing to apply your skills to, and undertake training on, biological systems if you do not already have a background in this area. We will provide training into the laboratory techniques if you do not have prior experience in that area.
This project offers:
- The chance to lead research papers
- Opportunities to use and develop skills in the growth of cultured tissue, an area of growing academic and industrial interest
- Opportunities to develop skills in biophysical models of tissues and deep learning techniques
Key Responsibilities
- To undertake training to run CONDOR simulations and in biophysics as required.
- To run and analyse CONDOR simulations on our high-performance computing cluster.
- To develop a deep-learning model for engineered tissue, train and validate it.
- To document this model.
- To contribute to writing papers and give presentations (at e.g. conferences and at the OU) as required.
- To contribute to the lively academic environment in the School of Physical Sciences.
- Maintain cell cultures and perform imaging studies and analysis on these cultures.
About You
Essential:
- A PhD in physics, biology, applied mathematics, computing or a closely related field (PhD candidates who have submitted or are about to submit their thesis will be considered)
- Experience of machine learning frameworks (e.g. TensorFlow)
- Knowledge of Python and C++
- Ability to communicate research results effectively
- Good oral and written communication skills
- Can demonstrate being a good team worker and able to work under own initiative
- Experience with biophysical simulation
- A willingness to work on biological systems and undertake training in that area if required
Desirable:
- Familiarity with the Linux operating system
- Experience in microscopy and / or wet biology
- A record of peer-reviewed publications and/or conference submissions commensurate with career stage
Support with your application
If you have any questions, or need support or adjustments relating to your application, the recruitment process, or the role, please contact us on or email quoting the advert reference number.
What's in it for you?
At The Open University, we offer a range of benefits to recognise and reward great work, alongside policies and flexible working that contribute towards a great work life balance. Get all the details of what benefits we offer by visiting our Staff Benefits page(clicking this link will open a new window).
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 works best for you.
Next steps in the Recruitment process
If shortlisted, we anticipate interviews taking place on either Friday 31st October or Monday 3rd November.
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.
How to apply
To apply for this role please submit the following document(s):
- CV (this should include a Publication List also).
- Supporting statement, outlining how you meet the essential and desirable criteria listed above.
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