University of York

Parallel Hybrid Solving of Challenging Decision Problems

  I'm a Computer Science Student at York, graduating in 2026. I enjoy software development (after a year in industry at the Wellcome Sanger Institute), and algorithmic approaches to solving problems (specifically using constraint programming).

Why did you apply for this internship?

I especially enjoyed my dissertation (focussed on using constraint programming), so it was particularly exciting to have the opportunity to do a project where I could work on a new constraint programming challenge.

What do you hope to gain in completing this project?

First-principles prediction of surface and nanocrystal properties of BaCd2P2 for optoelectronic applications

Why did you apply for this internship?

Having the opportunity to gain first-hand experience of the methodologies for research is invaluable to me, since I want to pursue research further - in my final year and prospective PhD. Doing this whilst contributing to an impactful project is an incredible opportunity, it is something I would be extremely proud to be a part of. Making what I thought to be out-of-reach for my current stage possible, I truly believe this internship is the perfect opening for me and my development.

What do you hope to gain in completing this project?

This internship program feels like the appropriate step towards being truly capable as a researcher, gifting the right amount of independence at the right time to utilise such - being in the last years of my degree means I have enough knowledge of my subject to actively contribute to the field in this way. As a supplement to these practical skills and experience, the confidence this entire internship process would give me is unparalleled compared to anything I have done before. Not only being able to generate a publishable report, but also the smaller steps no one recognises to be as much of a boost to ones confidence.

This internship is allowing me to realise the future I want; the experience I will obtain, the skills I will be able to develop and the confidence I can build throughout this process are accolades I know are invaluable towards my goals!

Automated Biomechanical Pipelines for Personalized Diaphragmatic Assessment

High-Performance Proteomics at Scale: An R Package and Interactive Web Portal for Standardised Analysis

I am a final-year integrated master's student in Biomedical Science, with experience in both experimental and computational research. My academic experience has allowed me to begin building my own tools to answer biological questions and has taught me that data generation, processing and analysis are inseparable. Having a robust analytical infrastructure can overcome experimental limitations that simply collecting more data cannot fix.

My current project involves analysing single-cell data in a paediatric cancer context, where samples span patients across a wide range of ages and biological backgrounds. While the pipeline I am building accounts for this variation, the process exposed a broader challenge: without well-maintained, standardised tools, researchers often write their own independent scripts. This can lead to inconsistent conclusions from the same dataset and a lack of reproducibility, particularly when there is a lack of specialised computational training.

I saw the solution to this challenge firsthand during a summer internship at the Edinburgh Biobank, where I contributed to the making of AI-based prognostic tools for breast cancer. While the data was complex, the use of well-designed computational pipelines meant that analyses were naturally reproducible and scalable. This experience served as a powerful contrast to the common bottleneck in modern research. It made me realise that the quality of our tools is often what determines the reliability of our insights, rather than the data generation itself.

These experiences have changed my perspective on research impact. I now see that building infrastructure that enables others to do rigorous research is not a supporting role, it is a fundamental role in the scientific process. This shift, from focusing solely on biological questions to also prioritising the tools that make them answerable, is what draws me to explore a career as a data research professional.

Why did you apply for this internship?

I was especially interested in this project as it aims to make computational methods more accessible to a wider audience, enabling researchers to make the most of their experimental results.

What do you hope to gain in completing this project?

In addition, I am excited to learn more about the role of a Research Software Engineer and gain insight into potential career paths that combine computational and biological research.