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Statistics PhD Projects, Programs & Scholarships in Cambridge

We have 15 Statistics PhD Projects, Programs & Scholarships in Cambridge

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  Accounting for heterogeneity in network neuroscience: extending the Bayesian Exponential Random Graph Model to infer and identify group differences
  Dr S White, Dr B Tom, Dr B Lehmann
Application Deadline: 7 January 2020

Funding Type

PhD Type

Background. Network Neuroscience is the study of the structure and function of the brain through the lens of networks and network constructs [1,2].
  How to detect changes in cognition trajectories: longitudinal study design to efficiently estimate biomarker change-point outcomes and time-to-change-point
  Dr S White, Dr B Tom, Dr B Lehmann
Application Deadline: 7 January 2020

Funding Type

PhD Type

Background. It is common to make longitudinal observations, repeated measurements over time, of biomarkers (and other covariates).
  Trajectories of modifiable risk factors and their influences on disease outcomes: using genetics in life course epidemiology
  Dr S Burgess, Dr J Barrett
Application Deadline: 7 January 2020

Funding Type

PhD Type

Questions about how risk factors influence disease outcomes are fundamental to epidemiology. However, these questions are difficult to answer, as relevant data are usually observational, and thus associations are not necessarily a reliable guide for causal relationships.
  Modelling the effect of gender on survival in cystic fibrosis patients
  Dr J Barrett, Dr B Tom
Application Deadline: 7 January 2020

Funding Type

PhD Type

Cystic fibrosis (CF) is a genetic condition affecting more than 10,000 people in the UK. Cystic fibrosis patients commonly experience respiratory symptoms, but the disease can also affect the digestive system, liver and bones.
  Epidemic modelling with online model assessment: value of information and conflict
  Dr A Presanis, Dr C Jackson, Dr D DeAngelis
Application Deadline: 7 January 2020

Funding Type

PhD Type

For a fast-moving infectious disease such as influenza, whether seasonal or pandemic, or a newly emergent infectious disease, the ability to quickly evaluate the current spread of disease is crucial for understanding and forecasting the burden, particularly severe burden, on public health services such as GPs and hospitals.
  Improving statistical methods for trial emulations using observational data
  Dr L Su, Dr S Seaman
Application Deadline: 7 January 2020

Funding Type

PhD Type

Randomised controlled trials (RCTs) are regarded as the gold standard for estimating causal effects of treatments on health outcomes.
  Dynamic prediction of in-patient mortality based on electronic health record data: a comparison of landmarking and machine learning approaches
  Dr S Kiddle, Dr J Barrett
Application Deadline: 7 January 2020

Funding Type

PhD Type

Background. There is great potential to use electronic health record (EHR) datasets to improve care of patients, as EHR are typically bigger, longer and more representative of the healthcare population than traditional research cohorts.
  Evidence synthesis in health impact models for interventions to prevent chronic diseases
  Dr C Jackson, Dr A Presanis, Dr D DeAngelis
Application Deadline: 7 January 2020

Funding Type

PhD Type

Many public health interventions are designed to reduce the risk of more than one disease, for example, interventions related to physical activity or diet, or general health checks.
  Modelling the association between blood pressure variability and cardiovascular disease
  Dr J Barrett
Application Deadline: 7 January 2020

Funding Type

PhD Type

Cardiovascular disease is a leading cause of mortality in the UK, with over 150,000 cardiovascular-related deaths each year. By identifying novel cardiovascular risk factors we can improve cardiovascular risk prediction, so that individuals at greatest risk may benefit from preventative intervention.
  Data-adaptive approaches for causal inference
  Dr S Burgess
Application Deadline: 7 January 2020

Funding Type

PhD Type

Identifying causal relationships is of vital importance for understanding disease processes, and for prioritizing risk factors as potential therapeutic targets.
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