General description of the XDF programme. The Cross-Disciplinary Fellowships (XDF) Programme at the University of Edinburgh has been initiated in response to recognition of interdisciplinarity and numeracy (in particular statistics and computation) as vulnerable skills and capabilities in UK postgraduate and postdoctoral research by multiple think-tanks, funders and research organisations. It is being implemented by the Institute of Genetics and Cancer (formerly the Institute of Genetics and Molecular Medicine) in close collaboration with investigators from the School of Informatics (SoI) and with contributors from other organisational units of the University. The XDF Programme aims to: Train truly cross-disciplinary future leaders in quantitative biomedicine, possessing high value analytical and computational expertise, and a newly-gained in-depth appreciation of biomedical and health research, Deliver impactful research findings on questions that are intractable to today’s IGC researchers, Provide a platform for transfer of high value skills and know-how between diverse disciplines, Facilitate formation of new collaborative links – between disciplines, industry, and academia – that would not otherwise be forged. The Programme has been designed to recruit highly trained early-career researchers from numerical disciplines such as physics, mathematics, statistics, computer science, engineering, or similar and provide them with tools and environment to become experts in biomedical subjects. At the same time the XDFs are expected to help training biologists and medics in advanced numerical subjects. Following introduction to life science research environment and initial guided and self-learning process complemented by practical experience gathering during first year rotation projects in diverse labs (each about 3-4 months long) the Fellows are expected to propose a more substantial research project in quantitative biomedicine and pursue it over the remaining three years of their fellowship (see the accompanying schematic for details). This article was published on 2022-09-28
