Cross-Cutting Themes

The cross-cutting theme covers four key areas, including: Data Science, Interdisciplinarity, Reproducibility and Integrity, and Translation and Innovation.

Data Science

Data science is present across all three research themes, where advanced computational and statistical methods are leveraged to analyse large-scale biomedical data, such as genomics, clinical records, imaging data and population cohorts. Students within the program receive comprehensive training in these methods, empowering them to design a systematic framework tailored to their individual hypotheses for data collection, cleaning, and analysis, with a strong emphasis on ensuring the reliability and reproducibility of results.


Interdisciplinary approaches are a key part of modern research and skills in this area are an increasingly important component to a research career. This cross-cutting theme aims to provide all our students with an understanding of interdisciplinarity, including its advantages, challenges and best practices.

Reproducibility and Integrity

The Reproducibility and Integrity cross-cutting theme aims to provide researchers with an understanding of the potential threats to research quality, and the solutions that exist to protect against these. This includes both good experimental design, and a range of open research practices, including sharing study protocols (e.g., pre-registrations), intermediate research outputs (e.g., data and code), and making use of open access publishing options (e.g., preprints).

Translation and Innovation

The Translation and Innovation theme urges researchers to assess the impact of their projects and find ways to apply their findings beyond academia. Interacting with innovators and entrepreneurs from different fields helps researchers develop skills in project management, intellectual property/regulatory affairs, communication, business models/planning, funding, and networking. 

Previous Cross-Cutting Projects