Advice and FAQs

A: From physics, chemistry, mathematics, statistics, engineering, computer sciences or similar. If you have a PhD in one of these disciplines and are motivated to follow a biomedical career, then apply: we are open-minded if you are.

A: Yes. Previous experience in computational life sciences is not a problem. Skills and motivation are most important.

A: Yes, applications from suitably qualified candidates from all around the world will be considered.

A: Yes. We will always consider individuals’ personal motivations and circumstances.

A: No. However, we will be looking specifically for scientists whose desire for this career change is well motivated and thought-through.

A: Yes, from lab-based protocols, basic molecular/cellular concepts, and evolutionary principles, to next generation sequencing, electronic health records and high-throughput drug screening.

A: In a person-centred manner according to skill or knowledge gaps, taking advantage of existing on-line and University courses, with one-to-one tutorials, seminars, and as part of a training cohort.

A: Biology and medicine are awash with very large data sets needing to be transformed into knowledge and hypotheses. Importantly, statistics and informatics have progressed to a level where such data sets can be analysed at scale creating the potential for significant breakthroughs. Analysis of such data will require team science and individuals with rare skills, each spanning traditional disciplines: a) recasting and linking data into usable forms; b) translating the jargon, tenets and assumptions of one discipline into another; and, c) identifying the optimal analytical approach and experimental design with which to answer the most important biomedical questions. There is a yawning skills gap, which this Fellowship scheme seeks to begin to close.

A: The projects will be chosen to match each Fellow’s research interests and expertise in analysing available data sets. Possible examples include: (1) genetic algorithms used with multiparametric high content phenotyping screening; (2) deep learning of retinal images from people with diabetes; (3) integration of multi-omic data from patients in order to define illness trajectories; (4) statistical modelling from population data to predict response to treatment; (5) text mining integrative analyses of GP notes and prescription data; (6) deep convolutional neural networks to learn protein-DNA binding sites; (7) machine learning to predict the impact of somatic mutations on the cancer epigenome; (8) estimation of cancer risk and drug targets for chemoprevention; (9) statistical modelling of a Scotland-wide ovarian cancer dataset; (10) predictions of individuals’ responses to cancer immunotherapy, and, (11) high content image analysis of biomedical data (e.g. images of pathology sections).

A: Eventually, Fellows are expected to take a leading role in their chosen project, taking advantage of the skills, interests and expertise of a cross-disciplinary research team.

A: Fellows will receive postdoctoral-level salary linked to their skills-base and experience.

A: Fellows are expected to successfully apply for independent Fellowships or positions following their 4 year XDF.