These projects are varied in focus, and we greatly enjoyed learning about the research in our Showcase on the 12th September, hosted at the National Centre for Early Music in York. If you are interested in finding out more, please read our news article about the day.
The university links below show the projects, a student bio and their expectations before starting their internship. To learn about the conclusions of their research, see their slides, and watch their presentations, follow their personal link.
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Reduced order modelling of land subsidence |
Arthur Chai |
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Visualising and exploring contextualized Arts and Humanities collections with their scholarly articles |
Samuel Waugh |
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gemlib: a Python library for epidemic modelling |
Ben Hardy |
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Pipeline for fitting thermal responses of mosquito traits |
Teleri Stone |
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Applying new methods for historical spelling normalisation to Early English books online |
Will Cook |
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What do Large Language Models actually know? |
Jenelle Bankas |
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From Modern Tweets to Victorian Broadsheets: Sentiment Analysis Software for Victorian Literature |
Maddison Willetts |
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Wardsonar technical development |
Mysha Zaman |
Find out more
Learn how the Leeds 2025 RSE interns tackled real research challenges while developing technical skills that will shape their future careers on their web page.
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Music.ME |
Al Aiham Ahmed Al Ma’Amari |
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Developing Transformer based Molecular Classification model |
Hamzah Patel |
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AI tools for large-scale text mining PubMed |
Zichen Yang |
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Advanced data analysis in the Humanities |
Manoj Manikandan |
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Sustaining computational research in the Humanities |
William Roper |
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Association between modifiable lifestyle factors and onset of multiple long-term conditions, with a view to causality |
Yufei Lu |
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Advancing Predictive Pathway for Maternal Mental Health Recovery: Machine Learning, Model Integration, and Scalable Deployment of Recovery Trajectories. |
Daniela Sanaicele Perez |
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Easing access to high-throughput GPUs for Deep Learning researchers |
Tom Reece |
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Advancing Statistical and Mathematical Validation for Maternal Mental Health Recovery Pathway: Deep Learning, Evaluation, and Evidence-Based Insights |
Zain Safdar |
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Improving data standardisation tools for global neuroscience collaboration |
Ariana Williams |
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PolyChron: FAIR data for past excavation analysis |
Shreyan Ghosh |
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AI quantum entangled PET - exploring a transformative future for healthcare |
Kiran Pooni |
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Machine Learning Gauge-dependent Density Functionals: A Challenge even in a Fixed Gauge |
Leo Arnstein |
The results of this project have been published in NPJ Computational Materials
Arnstein, L., Wetherell, J., Lawrence, R. et al. Addressing hypersensitivity in density functional theory for reliable machine learning. npj Comput Mater (2026). https://doi.org/10.1038/s41524-026-02191-x
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Helping Advance the History of Agriculture through Optimised Ancient DNA Analysis |
Quinn Cuff |