Research Fellow mathematical modelling of the tumour microenvironment Edinburgh, Scotland
Job description
Grade UE07: £35,333-£42,155 per annum
CMVM / MGPHS / USHER Institute / CMI
Full-time: 35 hours a week
Fixed-term: available from April 2023 to 31 March 2026 initially with option for 2 year renewal if further funding confirmed
Vacancies available: 2
The Centre for Medical Informatics at the Usher Institute within The University of Edinburgh is looking for 2 Research Fellows to conduct and facilitate original research as key members of a team of researchers with the UKRI Frontier Research Guarantee project μKinetic (originally a successful ERC Consolidator Grant). This includes responsibility for delivering core elements such as input to strategic research planning and management, conducting research on a collective basis with responsibility for delivering results within the project timelines. More specifically: a) the development of novel approaches to phenotyping the structure and temporal evolution of tumour vascular networks in animal models of glioblastoma multiforme (BGM) through the application of principles of Graph Theory, Topological Data Analysis, and Machine Learning [1,2]; and/or the development of mathematical models for the prediction of tissue oxygenation and drug penetration at the micrometre scale and model calibration/validation in a preclinical model of GBM [3,4], b) processing of wet-lab imaging data for quantification, mathematical model initialisation, and parameter estimation, c) running computer simulations and interpreting the results in order to aid identifying coarse-grained structural predictors of tumour tissue oxygenation and drug penetration in collaboration with other μKinetic researchers.
[1] https://doi.org/10.1016/j.devcel.2022.09.002
[2] https://doi.org/10.3934/fods.2021027
[3] https://doi.org/10.1073/pnas.2007770117
[4] https://doi.org/10.1073/pnas.1807105115
The Opportunity:
μKinetic sets off to revolutionise the treatment of glioblastoma multiforme (GBM) by delivering a radically new approach to the discovery of treatment biomarkers. GBM is the commonest primary brain tumour in adults, with a dismal median survival unchanged over 15 years. Tumour microenvironment is a key driver of GBM chemoradioresistance. However, there are no spatially resolved microenvironmental biomarkers associated with prognosis and treatment response prediction. For example, radiotherapy is delivered agnostic to tissue oxygenation, and investigational drugs are often selected on dichotomous criteria for brain penetration, without considering variable pharmacokinetics (PK) via aberrant vasculature.
μKinetic will address key gaps in GBM understanding with four ambitious objectives:
1. To structurally phenotype tumour vasculature and its temporal evolution.
2. To predict tumour tissue oxygenation and PK at the micrometre scale.
3. To validate predictions in a preclinical model of glioblastoma.
4. To identify coarse-grained structural predictors of tumour tissue oxygenation and PK.
Such structural biomarkers (measurable in patients through biopsy or next-generation imaging) have the potential to become a transformative tool for personalised cancer treatment planning.
Informal enquiries may be directed to Prof. Miguel O. Bernabeu, Professor of Computational Medicine ([email protected]).
- PhD (or near completion) in a relevant discipline e.g. Mathematics, Physics, Computer Science or Engineering.
- Experience in the segmentation and quantification of vascular phenotypes in angiograms; or experience in the development of multiscale mathematical models of blood flow and mass transport and their calibration/validation based on experimental data.
- Experience working with wet-lab imaging data (image processing and quantification).
- Competence in object-oriented computer programming in at least one of the following languages: C++, Python, Java.
- Proven track record of publishing research outputs.
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Please include your CV and a supporting statement with details of how you meet the knowledge, skills and experience required for this post.
As a valued member of our team you can expect:
An exciting, positive, creative, challenging and rewarding place to work. We give you support, nurture your talent and reward success. You will benefit from a competitive reward package and a wide range of staff benefits, which includes a generous holiday entitlement, a defined benefits pension scheme , staff discounts, family friendly initiatives , flexible working and much more. Access our staff benefits page for further information and use our reward calculator to find out the total value of pay and benefits provided.
The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality.
If invited for interview you will be required to evidence your right to work in the UK. Further information is available on our right to work webpages.
Work with us | Usher Institute - where inquiring minds work together to improve lives across the world.
Join our growing team of academics and professional services staff and work in a vibrant, multi-disciplinary environment. Our vision is to catalyse the transformation of health in society by working with people, populations and their data.
Our greatest asset is our people, working together to improve public health using evidence-based research.
We are a key applied and translational arm of the Edinburgh Medical School, within the College of Medicine and Veterinary Medicine at The University of Edinburgh. Working with partners across UK and international universities, health and care providers, policymakers, industry and the public is central to our highly collaborative ethos.
The University is able to sponsor the employment of international workers in this role. If successful, an international applicant requiring sponsorship to work in the UK will need to satisfy the UK Home Office’s English Language requirements and apply for and secure a Skilled Worker Visa.
