Research Associate, Data Scientist, Research Scientist

Job details

Posted 11 February 2026
Salary Grade 7: £41,064 - £46,049 per annum
End date 11 March 2026

Location:

Glasgow
Job Type: Research and Teaching
Reference: 192914
Expiry: 11 March 2026 at 23:45

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.

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

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.

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.