QUEST Programme

Are you a talented individual with a PhD in statistics, applied mathematics or computer science? We want to provide you with the environment to further develop your skills to address challenges in areas where we are acknowledged internationally as research leaders: stratified medicine, infections, regenerative medicine and public health.

Fellows will receive relevant training and supervision in at least two of the quantitative skills areas of statistics, computer science and applied mathematics, whilst being part of a team undertaking research in one of the biomedical areas described above.

Apply Now

Expression of Interest by email

31st January 2019

Applications Close

17th May 2019



Start the Fellowship

June 2019 - March 2020

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Fellow Profile

Name: Maria Sudell

Data synthesis over a network of multiple treatment comparisons for joint longitudinal and event-time outcomes
Related longitudinal and time-to-event data is often collected from multiple heterogeneous data sources (such as studies, hospitals or centres) to answer clinical questions. Using such data, it is of interest to simultaneously assess all available treatment options, rather than perform multiple pairwise comparisons. However such analyses... Read More

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Fellow Profile

Name: David Hughes

Approximation Approaches for Efficient Clinical Predictions
High-dimensional data is now routinely collected in many settings, due to the number of different variables being measured on a subject, the number of times a variable is measured and the number of individuals in a study. This data can be used for screening patients to determine their risk of disease, or to classify patients into risk groups... Read More

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Fellow Profile

Name:Maya Wardeh

Big Data approaches to identifying potential sources of emerging pathogens in humans, domesticated animals and crops
Emerging infectious diseases continue to pose major threats to humans, animals and plants. Recent years have seen significant outbreaks of several emerging diseases, ranging from the well-known (Ebola and Olive quick decline syndrome), to the previously little known (Zika), to the entirely novel (Schmallenberg), to name but a few...