Open University
1 year ago
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Research in the use of machine learning for predicting and analyzing cell organization in tumoroids Open University in United Kingdom
Degree Level
Postdoc
Field of study
Cell Biology
Funding
not provided
Deadline
Sep 30, 2026
Country
United Kingdom
University
Open University
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Keywords
About this position
This postdoctoral position at The Open University focuses on research into the use of machine learning informed by biophysical simulations to predict and analyse cell organisation in tumoroids. The project aims to understand and eventually control the arrangements of cells in spheroids and tumoroids, which is crucial for the correct functioning of biological tissues. The successful candidate will develop and test machine learning techniques based on biophysical simulations, grow tumoroids in the laboratory, and perform confocal microscopy to establish cell organisation.
These experimental data will be used to validate advanced predictive techniques, building on the CONDOR simulation method. The role involves using mature simulation code, requiring proficiency in Python (with machine learning frameworks such as TensorFlow) and C++. Some knowledge of Linux is desirable, and training in laboratory techniques will be provided if needed. Key responsibilities include running and analysing simulations on high-performance computing clusters, developing and documenting deep-learning models for engineered tissue, maintaining cell cultures, performing imaging studies, and contributing to research papers and presentations.
The position offers opportunities to lead research papers, develop skills in tissue growth and biophysical modelling, and participate in a vibrant academic environment. Applicants should have a PhD in a relevant field, experience with machine learning and biophysical simulation, and strong communication and teamwork skills.
The Open University is committed to equality, diversity, and inclusion, and encourages applications from underrepresented groups. The contract is fixed-term until 30 September 2026, with flexible working arrangements available. Early applications are encouraged as the advert may close before the published deadline.
What's required
Applicants must have 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. Essential skills include experience with machine learning frameworks (e.g., TensorFlow), knowledge of Python and C++, ability to communicate research results effectively, good oral and written communication skills, ability to work both independently and as part of a team, and experience with biophysical simulation. Candidates should be willing to work on biological systems and undertake training in that area if required. Desirable qualifications include familiarity with the Linux operating system, experience in microscopy and/or wet biology, and a record of peer-reviewed publications and/or conference submissions commensurate with career stage.
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ApplyKite TwinLearns as you applyMake your next move.
Prepare your application
Review drafts and confirm requirements with the university.