Health Data Science PhD

University of Liverpool

Undertake research in cutting edge data science methods and learn how these can be used in a wide variety of health-related applications on this research degree in health data science. Projects range from state-of-the-art algorithm development through to applying machine learning, artificial intelligence or statistical methods in groundbreaking biomedical research.IntroductionThe Department of Health Data Science, within the Institute of Population Health, produces cutting-edge data science research.The Institute has strong links with civic health collaborators through Civic Health Innovation Labs (CHIL), an interdisciplinary research centre. This provides our postgraduate researchers with excellent access to policy makers, ensuring a dynamic experience of using data science methods to inform policy decisions.The Department of Health Data Science undertakes high-impact research in statistical genetics, pharmacogenetics, joint modelling of longitudinal and time-to-event data, multivariate data analysis, stereology, multi-source evidence synthesis and clinical trials.The department also boasts a sustained track record of research funded by the Medical Research Council, Wellcome Trust, Horizon 2020 and the National Institute for Health and Care Research (NIHR), and is committed to excellence in methodological and applied research.This provides an ideal environment for postgraduate researchers to develop cutting-edge machine learning, statistical and data science skills. You’ll have the opportunities to apply these skills in tackling important health challenges such as stratified medicine, infections, regenerative medicine and public health.Research topicsWe welcome proposals from potential postgraduate researchers with their own topic ideas. We have expertise in supervising projects across a range of topics including:Machine learningArtificial intelligenceClinical trialsMultivariate and multilevel data analysisBayesian modellingMethods for evaluating biomarkersStatistical pharmacogeneticJoint modelling of longitudinal and time-to-event dataPharmacokineticsPharmacodynamics personalised dosing algorithmsPrediction modellingCausal Inference.Research culture