MRC/UoE Cross Disciplinary Fellowships (XDF)
The University of Edinburgh

Closing date: 8th April 2018

Do you wish to grow your analytical skills to make the next breakthrough in biomedical research?

We are seeking up to 8 fellows who have acquired strong data analytical and/or computational skills from their doctoral studies in physics, mathematics, computer science, engineering, or similar.

Our pledge: We will train you to become a leader in Big Data Quantitative Biomedicine.

Our request: You will need to want to solve the most pressing questions in biomedicine.

If you have these analytical skills and seek to broaden your scientific horizons and make a real impact on people's health then please do apply.


The beginning of the 21st century has seen enormous advances in science and technology. With the completion of the Human Genome Project and implementation of multiple “Big Data” approaches in biomedical sciences, there is now a pressing need to train a new generation of mathematically-minded biomedical scientists who will be able to bridge the gap between life sciences and mathematics/physics/informatics, and efficiently link modern biomedical research with big data research technologies. To address this need a pioneering Cross-Disciplinary Post-Doctoral Fellowships programme (XDF) has been initiated at the University of Edinburgh with matching financial support from the Medical Research Council.

The University of Edinburgh is one of the world leading research universities (ranked 4 th in UK for its research power) and is at the forefront of both computational sciences and health sciences. Informatics is the largest and strongest computer science department in the UK (1 st for research power according to REF2014), with particular strengths in data science and computational biology. Clinical medicine has been ranked 4 th in the UK (research power) with Institute of Genetics and Molecular Medicine (IGMM) being one of the biggest biomedical research establishments in the country. The XDF Programme lead, Prof. Ponting, was trained first in particle physics before pursuing a successful career in biomedicine, so knows first-hand the skills necessary for Fellows to transition into "Big Data Biomedicine". The programme will be led by a board of Directors, including Profs Jane Hillston and Guido Sanguinetti (School of Informatics) and Profs Margaret Frame and Tim Aitman (School of Molecular, Genetic and Population Health Sciences/IGMM), who will provide Fellows with diverse perspectives.


The fellowships are aimed at early-career quantitatively trained scientists, whose ambition is to achieve an independent career in data-driven computational biomedicine. Fellows will follow a personalised training and research programme to become truly cross-disciplinary leaders in quantitative biomedicine. Fellows will gain analytical and computational expertise, and an in-depth appreciation of biomedical and health research. Fellows will be motivated to address biomedical questions, to apply and train others in their previously acquired analytical/computational skills, and to learn the strengths and limitations of biomedical science methods. Fellows will propose a well-developed, important and innovative biomedical project only after substantial relevant training.

Fellowships are funded jointly by the MRC and the University of Edinburgh. Fellows will receive mentorship from both computational and biomedical scientists, and will be offered office space in both Informatics and IGMM. Where appropriate, the research may also be conducted in collaboration with an industrial partner and/or the NHS. After their initial year, the Fellow will conduct original research and produce material for peer- reviewed publications and for dissemination at national and international level.

All applicants should apply online. The application process is quick and easy to follow, and you will receive email confirmation of safe receipt of your application. The online system allows you to submit a CV and other attachments.

**Mention you saw it on the AustMS website**