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MRC Biostatistics Unit- 6 funded PhD Opportunities
martes, 17 de diciembre de 2019

MRC Biostatistics Unit- 6 funded PhD Opportunities*
The BSU is an internationally recognised research unit specialising in statistical modelling with application to medical, biological or public health sciences.
We are currently recruiting for 4 (four) MRC funded BSU studentships and 2 (two) NIHR studentship.
To apply for any of the following PhD projects, please visit .
Deadline for all application information to be received by the University application system is *Tuesday 7th January 2020*
All informal enquires to be directed to Esta dirección de correo electrónico está protegida contra los robots de spam, necesita tener Javascript activado para poder verla
We currently have the following projects available at the MRC Biostatistics Unit.
• Data-adaptive approaches for causal inference– Stephen Burgess
• Modelling the association between blood pressure variability and cardiovascular disease– Jessica Barrett
• Bayesian dose adaptive trials using non-myopic response-adaptive methods – Sofia Villar and David Robertson
• Evidence synthesis in health impact models for interventions to prevent chronic diseases– Chris Jackson, Anne Presanis and Daniela De Angelis
• Accounting for heterogeneity in network neuroscience: extending the Bayesian Exponential Random Graph Model to infer and identify group differences– Simon White, Brian Tom and Brieuc Lehmann (University of Oxford)
• Conditional false discovery rates in high dimensional data sets– Chris Wallace
• Dynamic prediction of in-patient mortality based on electronic health record data: a comparison of landmarking and machine learning approaches– Steven Kiddle and Jessica Barrett
• Improving statistical methods for trial emulations using observational data– Li Su and Shaun Seaman
• Clinical prediction under informative presence: Exploring what the patterns and timing of repeated observations in routinely collected data can tell us about disease risk– Jessica Barrett and Brian Tom
• Statistical and machine learning analysis of images of platelet formation– William Astle and Cedric Ghevaert (University of Cambridge)
• Developing Bayesian non-myopic response-adaptive randomisation for the case of delayed endpoint observation – Sofia Villar and David Robertson
• Trajectories of modifiable risk factors and their influences on disease outcomes: using genetics in life course epidemiology– Stephen Burgess and Jessica Barrett
• Epidemic modelling with online model assessment: value of information and conflict– Anne Presanis, Chris Jackson and Daniela De Angelis
• Modelling the effect of gender on survival in cystic fibrosis patients– Jessica Barrett and Brian Tom
• How to detect changes in cognition trajectories: longitudinal study design to efficiently estimate biomarker change-point outcomes and time-to-change-point– Simon White and Brian Tom.