Browse our 2025 cohort student profiles below. Tesni Walsh ResearchAI and the Dark Genome: Using protein-DNA structure modelling and genomic language models to predict the impacts of non-coding genetic variationSupervisorsJoseph Marsh, Simon Biddie Mohammad Kouli ResearchAI-driven continuous physiological monitoring to predict deterioration following surgerySupervisorsEwen Harrison, Annemarie Docherty I’m interested in modelling waveform data to predict postoperative deterioration. Most of my time is spent wondering whether it’s physiology or just another artefact. Mario Navarro Veiga ResearchGenetic regulation of antibiotic resistance in the major pathogen Klebsiella pneumoniaeSupervisorsAndrea Weisse, Thamarai Dorai-Schneiders Hailey Deckers ResearchStratifying cancer treatment responses in mesothelioma with AI-driven bioimagingSupervisorsCarsten Hansen, Yunjie YangStudentship in partnership with NHS Lothian During my PhD, I integrate wet-lab and computational methods to uncover insights to biomedical data. By studying the cellular morphology of Pleural Mesothelioma, I aim to develop new hypotheses that advance the development of effective therapies. Stephen Binaansim ResearchFinding the rhythm: detection of metabolic events in mental health conditions from time series dataSupervisorsKarl Burgess, Diego Oyarzun My PhD research is driven by the question of how AI can help us understand the hidden rhythms of human biology. I hope to develop computational methods that transform complex metabolomics and time-series data into actionable insights for biomedical and mental health applications. My Website Hannah Jiang ResearchAI-driven investigation of the neural circuit dynamics supporting online motor adaptationSupervisorsIan Duguid, Angus ChadwickStudentship in partnership with Simons Initiative for the Developing Brain (SIDB) I'm fascinated by using mathematical and computational models to understand the mechanisms of the brain, and by how AI and neuroscience can inform one another. Sim Mei Choo ResearchTowards Operationalisable Clinical Risk Prediction ModelsSupervisorsSohan Seth, Bruce Guthrie I am passionate about helping healthcare AI become more trustworthy and practical in real-world settings. Eleanor Harrison ResearchHow do different ways of making a home warmer affect risk of preschool respiratory infections? Using artificial intelligence to make homes and children healthier.SupervisorsOlivia Swann, Sohan SethStudentship in partnership with the Department for Energy Security & Net Zero My goal is to create impactful housing policy recommendations that will improve quality of life for families and reduce health inequalities here in Scotland. Anthos Makris ResearchDiscover novel imaging features in OCTs and/or statistical data that predict visual outcome after macular hole surgery and that can be used to inform clinical decision makingSupervisorsHeather Yorston, Stuart King Nardiena Pratama ResearchExplainable and Transparent AI models for Glioma Diagnosis from Brain MRISupervisorsAjitha Rajan, Paul Brennan I am developing advanced decision-support tools to improve patient outcomes, with a current focus on brain cancer. More broadly, my ultimate goal is to design methodologies that can benefit a wider range of diseases. My Website Joanne Igoli ResearchCausal healthcare analytics for Real-World Evidence with Targeted Learning: A cross-disciplinary, cross-sector approachSupervisorsSjoerd Beentjes, Ava KhamsehStudentship in partnership with NICE Iva Jankovic ResearchLearning Algorithms for Discovering Synthetic Lethal Metabolic Interventions in CancerSupervisorsFilippo Menolascina, Vissarion Fisikopoulos Nikoo Moradi ResearchArtificial Intelligence EEG Biomarkers for Neurodevelopmental DisordersSupervisorsAlfredo Gonzalez-Sulser, Javier EscuderoStudentship in partnership with Simons Initiative for the Developing Brain (SIDB) What excites me most is turning something as messy as an EEG signal into an objective measure of brain health — so that children with a rare, untreatable disorder can finally have their progress measured, and one day their treatments proven. My Website Lachin Soufizadeh ResearchUsing machine-learning approaches to predictively genotype ASD model rats based upon large-scale, high-density neuronal network dataSupervisorsPeter Kind, Paul RignaneseStudentship in partnership with Simons Initiative for the Developing Brain (SIDB) I'm fascinated by the potential of AI to uncover subtle patterns in neural recordings that would otherwise go undetected, bringing us closer to understanding the neural mechanisms underlying neurodevelopmental conditions and, ultimately, the people living with them. This article was published on Friday 6 March 2026
