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British Council Women In STEM Scholarships

In partnership with the British Council, Queen Mary University of London offers five scholarships for Women in STEM for the 2026-27 academic year. Access to education is one contributing factor leading to the underrepresentation of women in STEM. The objective of this programme is to help address this situation by providing opportunities for women from South Asian countries to continue with their studies and research in STEM subjects. The scholarship covers full overseas tuition fees, a contribution towards monthly maintenance allowance travel, visa and other initial costs. Successful applicants must demonstrate a need for financial support, have not previously studied at degree level or higher in the UK, and must agree to return to their country of citizenship for a minimum of two years after the scholarship award has ended. The programme seeks applicants who are active in their field, willing to demonstrate future contribution to capacity-building and socio-economic advancement, and can demonstrate a plan and passion to engage other women and girls in STEM from their home country.

Digital Twins for Liver Cancer Using Medically Informed Machine Learning

This PhD research project develops digital twin models for the human liver for use in liver cancer treatment planning and optimisation. The project creates an image-based computational model of the liver with realistic anatomical variability, structure, and aspects of functionality. Digital twins have many use cases including training clinicians, testing computational algorithms, planning patient-specific treatments, and enabling virtual in-silico trials for evaluating novel treatments. The project focuses on hepatocellular carcinoma (HCC), the most common type of primary liver cancer in adults and currently the most common cause of death in people with cirrhosis. The research will develop a liver function map as part of a digital twin model using biomarkers from pre-operative MRI to estimate tissue-level inflammation, fibrosis, fat content, cirrhosis, and hepatocyte uptake extraction rate. This combines magnetic resonance imaging with medically informed machine learning (MIML) techniques. The project seeks to exploit multi-modal imaging (CT, MRI) and novel data-driven machine learning methods to develop and validate the digital twin model. The liver function maps will be paired with anatomical shape models of the liver and computational algorithms for generating the vasculature of the liver and the tumour, creating the first ever digital twin model of the human liver. MIML techniques will be used to reduce model overfitting and ensure consistency with existing medical knowledge about tumour pathophysiology, vascular function, and tissue response to radiotherapy.