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Statistics (applied statistics) PhD Projects, Programs & Scholarships

We have 37 Statistics (applied statistics) PhD Projects, Programs & Scholarships

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Showing 21 to 30 of 37
  Which one should we choose? Developing statistical models to explain and forecast joint decision making
  Dr C Calastri, Prof S Hess, Dr R P Mann
Application Deadline: 31 January 2020

Funding Type

PhD Type

Mathematical models are used to interpret and forecast human behaviour in many disciplines, ranging from patient safety to energy, transport and marketing.
  Big Data driven treatments for Pulmonary Fibrosis
  Prof Gisli Jenkins
Applications accepted all year round

Funding Type

PhD Type

We invite applications for a 3-year PhD position, funded by the University of Nottingham to study ‘Big Data driven treatments for Pulmonary Fibrosis’.
  Statistical Data Analysis of medical health records
  Prof B Schelter, Prof M Thiel
Applications accepted all year round

Funding Type

PhD Type

In this project we will use advanced statistical and mathematical modelling techniques to analyse patient health records in order to support medical decision making.
  ONE Planet DTP - Integrated modelling to predict early signs of land degradation (OP20297)
  Dr M Montero-Calasanz
Application Deadline: 31 January 2020

Funding Type

PhD Type

2.2 million tonnes of topsoil is eroded annually in the UK and over 17% of arable land show signs of degradation (Fig.1). Reversing soil degradation and restoring fertility by 2030 is an aim of the government’s 25 Year Environment Plan but there are insufficient data on the health of UK soils.
  Develop new methodology for the analysis and reporting of meta-analyses in the presence of Heterogeneity
  Prof P Sasieni, Dr J Waller
Application Deadline: 7 February 2020

Funding Type

PhD Type

We have two studentships available across a possible four projects, which includes this project. The other three are available to view on Find a PHD or at www.kcl.ac.uk/health/study/studentships/div-studentships/cps/cps-studentship-projects.
  Modelling the impact of interventions in atrial fibrillation, the commonest cardiac rhythm disorder and a global healthcare problem
  Dr G Czanner, Dr I Olier, Prof P Lisboa, Prof G Lip
Applications accepted all year round

Funding Type

PhD Type

Project description. It is known that the risk of developing CVD is increasing in the UK along with the rest of the World. It is believed that the culprits are lifestyle and environmental factors.
  Statistical and machine learning methods for risk prediction of cardiovascular diseases from complex longitudinal data
  Dr G Czanner, Dr I Olier, Prof P Lisboa, Prof G Lip
Applications accepted all year round

Funding Type

PhD Type

Project description. Risk prediction models are used in clinical decision making and are used to help patients make an informed choice about their treatment.
  Bayesian Uncertainty Quantification for Clustering Problems
  Research Group: Statistics
  Dr W Yoo, Dr S Liverani
Applications accepted all year round

Funding Type

PhD Type

The School of Mathematical Sciences of Queen Mary University of London invite applications for a PhD project commencing either in September 2020 for students seeking funding, or in January 2020 or April 2020 for self-funded students.
  Integrative statistical inference methods for eukaryotic gene regulation with applications to embryonic stem cell differentiation
  Dr I Iqbal, Prof M Rattray, Prof A Sharrocks
Applications accepted all year round

Funding Type

PhD Type

Embryonic stem cells (ESCs) can differentiate into different cell types through intermediary cell states and deeper understanding of the regulatory control underlying these differentiation stages is a very important topic in the study of mammalian development (Yang et al., 2014 & 2019).
  Unsupervised outlier detection for high-dimensional data
  Dr O Isupova
Application Deadline: 10 February 2020

Funding Type

PhD Type

Recent advances in machine learning have already achieved superhuman performance in a wide range of applications. This success of machine learning relies on availability of large training labelled datasets.
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