PhD Studentship in Medicine
University of Nottingham
United Kingdom

Area: Medicine
Location: UK (Other)
Reference: MED2045
Closing Date: Wednesday 01 April 2026

Project Supervisors

• Michael Chappell

• Steffi Thust

Project Overview

Glioblastoma (GBM) is the most common and lethal adult brain tumour. Relapse is driven by infiltrative tumour cells that escape surgical resection and resist therapy. This PhD project focuses on the GBM infiltrative margin, developing advanced MRI analysis methods and imaging-driven predictive models.

Key elements include:

• Use of anatomical and physiometabolic imaging methods:

  • Arterial spin labelling perfusion

  • Neurite orientation dispersion and density imaging (NODDI)

  • Chemical exchange saturation transfer

• Integration of multiple modalities and parameters to identify at-risk sites for GBM relapse before clinical progression

• Development of cancer biomarkers through statistical and AI-driven models to predict aggressive tissue molecular signatures

• Linking imaging features to phenometabolic signatures from tissue samples collected during surgery

This project is part of the imaging theme for the new Nottingham Brain Tumour Research Centre of Excellence, combining advanced image analysis, mathematical modelling, cancer metabolomics, and novel physiological MRI technique development in a translational research environment bridging analytical bioscience and neuro-oncology.

Research Environment

• The PhD is based in the Brain Tumour Research Centre of Excellence at the University of Nottingham (5-year programme grant)

• Multidisciplinary partnership across the Schools of Medicine, Life Sciences, and Pharmacy

• Collaboration with:

  • Erasmus University Rotterdam, Netherlands

  • Mayo Clinic Arizona, USA

  • University of Freiburg, Germany

• Project location: Precision Imaging (School of Medicine)

  • 3T Philips Elition intraoperative MRI suite at Queens Medical Centre

  • Sir Peter Mansfield Imaging Centre (recently upgraded 7T MRI)

  • UK National Ultra High Field (11.7T) MRI facility opening during the project

Eligibility

• Strong undergraduate degree in relevant fields: Biomedical Sciences, Biomedical/Information Engineering, Computer Science, Analytical Bioscience, Physics, or related disciplines

• Prior experience in medical imaging, MRI, medical physics, or computational data analysis (Python/R/MATLAB, machine learning, bioinformatics) is highly desirable

Applications: Send CV to the provided contact. Consideration is on a rolling basis until the position is filled.

Deadline: 1 April 2026 for a September 2026 start

Funding

• 4-year PhD studentship

• Tuition fees covered for home students

• Annual stipend at current UKRI rates

Keywords: Brain cancer, glioblastoma, magnetic resonance imaging, advanced MRI, radiomics, machine learning

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