Job Purpose

To make a leading contribution to the BIOGRAPH – Biophysical Tumour Growth Modelling for Novel GBM Treatment Response Assessment: Harnessing Physics-Informed Deep Learning project, working with the Medical Image Analysis and AI research group within the School of Computing Science.

The successful candidate will develop novel computational frameworks integrating biophysical tumour modelling, multimodal neuro-imaging and physics-informed machine learning to improve assessment of glioblastoma treatment response. The candidate will also be expected to contribute to the formulation and submission of research publications and research proposals, and help manage and direct this complex and interdisciplinary project as opportunities allow.

Main Duties and Responsibilities

1.Take a leading role in planning and conducting research aligned with BIOGRAPH project deliverables.
2. Develop, implement and validate computational models of glioblastoma tumour growth and treatment response using physics-informed deep learning and data-driven modelling.
3. Design and implement pipelines for processing and analysing multi-modal neuroimaging data.
4. Document research outputs including data analysis, technical reports and journal publications.
5. Establish and maintain a strong research profile and contribute to the University of Glasgow’s research impact.
6. Survey research literature and develop suitable research strategies.
7. Present research at conferences, seminars and workshops.
8. Contribute to funding applications and future research proposals.
9. Develop collaborations with clinical, academic and industrial partners.
10. Support supervision and mentoring of students.
11. Perform administrative and data governance tasks.
12. Contribute to teaching activities where appropriate.
13. Maintain knowledge of advances in AI, computational modelling and translational medical technologies.
14. Engage in professional development and undertake other reasonable duties as required.
15. Contribute to enhancing the University’s international research profile.

Knowledge, Qualifications, Skills and Experience

Knowledge and Qualifications

Essential:

A1 Normally Scottish Credit and Qualification Framework level 12 (PhD) plus track record of emerging independence within a research/professional environment, or alternatively possess professional qualifications and experience equivalent to PhD level plus the requisite experience.
A2 Knowledge of mathematical and statistical methodologies including several of: Statistical modelling and inference, Bayesian statistics and probabilistic modelling, Inverse problems and parameter estimation, Uncertainty quantification and model calibration, Mathematical modelling of biological or physical systems, Machine learning and deep learning theory, Spatio-temporal and longitudinal data modelling, High-dimensional data analysis
A3 Strong knowledge of computational tools including: Differential equation-based modelling (ODE/PDE tumour models), Bayesian or Monte Carlo inference methods, Mathematical optimisation techniques, Physics-informed neural networks, Scientific programming in Python, MATLAB or equivalent, Analysis of complex multi-modal biomedical datasets

Skills

Essential:

C1 Project-Specific Skills: Strong mathematical and statistical modelling expertise, Ability to translate clinical and biological problems into rigorous computational models, Experience with mechanistic or hybrid mechanistic–data-driven modelling, Experience with parameter estimation, calibration, sensitivity analysis and uncertainty quantification, Ability to design statistically robust validation frameworks, Understanding of model identifiability, generalisation and validation, Ability to evaluate model reliability and clinical interpretability, Experience building reproducible computational workflows
C2 Ability to communicate complex mathematical and computational concepts to interdisciplinary audiences.
C3 Strong organisational and time-management skills.

Experience

Essential:

E1 Experience designing and conducting quantitative research using advanced mathematical or statistical modelling.
E2 Track record of peer-reviewed publications in applied mathematics, computational biomedical modelling, medical image analysis or machine learning.
E3 Experience applying statistical or mathematical modelling to complex datasets.
E4 Experience contributing to research funding applications.
E5 Experience supporting supervision or mentoring of students or junior researchers.
E6 Commitment to open and reproducible research practices.

Desirable:

F1 Experience applying modelling techniques to tumour growth or oncology.
F2 Experience in Bayesian modelling, probabilistic programming or uncertainty-aware machine learning.
F3 Experience with inverse modelling and data assimilation.
F4 Experience working with longitudinal clinical or imaging datasets.
F5 Experience in physics-informed machine learning or hybrid modelling.
F6 Experience collaborating with clinical or translational research teams.

Additional Project-Specific Qualities

- Strong interest in mathematically grounded modelling of biological systems
- Ability to work across theoretical mathematics, statistics and computational implementation
- Ability to critically evaluate model assumptions and clinical interpretability
- Enthusiasm for interdisciplinary research across computing, mathematics and healthcare
- Proactive, collaborative and solutions-oriented research approach

Terms and Conditions

Salary will be Grade 7, £41,064 - £46,049 per annum.

This post is full time (35 hours per week), and has funding available for up to 3 years in the first instance.  Relocation Assistant will be provide where appropriate.

The University of Glasgow has a responsibility to ensure that all employees are eligible to live and work in the UK.  If you require a Skilled Worker visa to work in the UK, you will be required to meet the eligibility requirements of the visa route to be assigned a Certificate of Sponsorship.

Please note that this post may be eligible to be sponsored under the Skilled Worker visa route if tradeable points can be used under the Skilled Worker visa rules. For more information please visit: https://www.gov.uk/skilled-worker-visa.

As a valued member of our team, you can expect:

1 A warm welcoming and engaging organisational culture, where your talents are developed and nurtured, and success is celebrated and shared.

2 An excellent employment package with generous terms and conditions including 41 days of leave for full time staff, pension - pensions handbook https://www.gla.ac.uk/myglasgow/payandpensions/pensions/, benefits and discount packages.

3 A flexible approach to working.

4 A commitment to support your health and wellbeing, including a free 6-month UofG Sport membership for all new staff joining the University  https://www.gla.ac.uk/myglasgow/staff/healthwellbeing/.

We believe that we can only reach our full potential through the talents of all. Equality, diversity and inclusion are at the heart of our values. Applications are particularly welcome from across our communities and in particular people from the Black, Asian and Minority Ethnic (BAME) community, and other protected characteristics who are under-represented within the University. Read more on how the University promotes and embeds all aspects of equality and diversity within our community https://www.gla.ac.uk/myglasgow/humanresources/equalitydiversity/.

We endorse the principles of Athena Swan https://www.gla.ac.uk/myglasgow/humanresources/equalitydiversity/athenaswan/ and hold bronze, silver and gold awards across the University.

We are investing in our organisation, and we will invest in you too. Please visit our website https://www.gla.ac.uk/explore/jobs/ for more information.

Closing Date:  11 March 2026