Cross-Disciplinary Fellowships

A post-doctoral fellowship programme for people with quantitative skills to address questions in biomedical research.

Application now open

Cross-Disciplinary Post-Doctoral Fellowship in Cancer Research (XDF) 

We are looking for an early-career quantitatively trained scientist with an ambition to achieve an independent career in data-driven quantitative cancer research. One (1) position available. This can be hosted or in collaboration with the CRUK Scotland institute. Application deadline: 11th December 2023.

Cross-Disciplinary Post-Doctoral Fellowship in Cancer Research (XDF) - University of Edinburgh Jobs Careers (

Cross Disciplinary Fellowships

A post-doctoral level Programme for physicists, chemists, mathematicians, statisticians, engineers, computer scientists etc. seeking training to become leaders in Quantitative Biomedicine.


The XDF Programme partners the School of Informatics with the Institute of Genetics and Cancer to train genuinely cross-disciplinary researchers.

XDF biomedicine

Biomedical data have already attained a scale, diversity and potential that are unprecedented.

XDF Programme

Analytically minded people with unique skillsets.

Abstract image of computers and cable

Tutorials, books, courses and other educational resources.

IGMM lecture theatre

Scientific events, social meetups, projects, publications and more.


The programme will be led by a board of Directors who will provide Fellows with diverse perspectives.

Cross Disciplinary Fellowships advice

Frequently asked questions and other useful information.

IGMM building skyline

Interdisciplinarity and innovation lies at the heart of the University.

Cross-disciplinary Research Environment

Systems biology must be embedded in a cross-disciplinary environment with biologists, chemists, computer scientists, engineers, mathematicians, physicists, and physicians. Our fundamental mantra is that leading-edge systems biology should drive the development of relevant technologies, and these, in turn, should push the creation of the necessary computational tools for handling the relevant data (often big data). These approaches have transformed our understanding of biological complexity.

Lessons Learned as President of the Institute for Systems Biology (2000–2018) Leroy E.Hood