University of Cambridge BSU PhD Studentship x 8 for UK, EU and International Students

The BSU is an internationally recognised research unit specialising in statistical modelling with application to medical, biological or public health sciences. Details of the work carried out in the Unit appear on our Research page.

We are currently recruiting for up to 6 MRC funded BSU studentships and 2 NIHR funded BSU studentships. The list of available projects is shown below. Applicants can select up to a maximum of three

supervisors/projects, although this is not a requirement. Applicants should have or expect to obtain the equivalent of a 2.1 degree. Whilst we would usually expect this degree to be in mathematics, statistics or a related discipline, we welcome applications from outside these areas provided applicants can demonstrate significant additional relevant experience within at least one of these areas. A Masters degree in statistics or a related discipline is considered desirable but not essential. Additionally, the Department requires that by the time of interview all potential students must have fulfilled the language requirements for admission.

Projects available specifically for the MRC Studentships

• Construction of multi layer gene regulatory networks to understand the human immune system - Chris Wallace
• Statistical inference for response-adaptive clinical trials - David Robertson, Sofia Villar and Adrian Mander
• Risk prediction of time to onset of Alzheimer's disease using imaging modalities within DPUK - Simon White and Brian Tom3
• Matching approaches for efficient and robust causal inferences in Mendelian randomization - Stephen Burgess and Brian Tom
• Evidence synthesis for estimating epidemiology of chronic diseases - Chris Jackson, Daniela De Angelis and Anne Presanis
• Dynamic risk prediction of cardiovascular disease using primary care data from New Zealand - Jessica Barrett and Angela Wood (CEU)
• Developing Bayesian non-myopic response-adaptive randomisation for the case of delayed endpoint observation - Sofia Villar and Adrian Mander
• Conditional false discovery rates in high dimensional data sets - Chris Wallace
• Influenza transmission modelling with online model assessment, including detecting and accounting for conflicting evidence - Anne Presanis, Chris Jackson and Daniela De Angelis
• Trajectories of modifiable risk factors and their influences on disease outcomes: using genetics in life course epidemiology - Stephen Burgess and Jessica Barrett
• 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 the robustness of mobile health trials through online false discovery rate control - David Robertson and Adrian Mander
• Developing methods to estimate HIV incidence and evaluate the role of prevention interventions in controlling transmission - Daniela De Angelis, Paul Birrell (PHE) and Anne Presanis
• Modelling the association between blood pressure variability and cardiovascular disease - Jessica Barrett and Stephen Kaptoge (CEU)
• Bayesian dose adaptive trials using non-myopic response-adaptive methods - Sofia Villar and Adrian Mander
• Longitudinal study design to efficiently estimate biomarker change-point outcomes and time-to-change point - Simon White and Brian Tom
• Modelling the sex effect on survival in cystic fibrosis patients - Jessica Barrett and Brian Tom

Projects available specifically for the NIHR Rare Diseases Studentships

• Statistical methods for integrating intermediate quantitative phenotypes in rare disease genetic association analysis - Ernest Turro (Department of Haematology), Will Astle (CEU and MRC BSU) and Daniel Greene (Department of Haematology and MRC BSU)
• Identifying the genetic determinants of translation rate in blood cells and characterising their relevance to human traits - Ernest Turro (Department of Haematology), Will Astle (CEU and MRC BSU) and Mattia Frontini (Department of Haematology)

The closing date for applications is 3 January 2019. Interviews will be held on Tuesday 22nd and Wednesday 23rd January 2019.

READ MORE AND APPLY HERE