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Statistics PhD Projects, Programs & Scholarships in Liverpool

We have 11 Statistics PhD Projects, Programs & Scholarships in Liverpool

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  Discovery of transparent conductors for sustainable buildings using Deep Learning (Reference NSGML2020PS)
  Prof M J Rosseinsky, Prof J Y Goulermas, Dr M Gaultois, Dr V Gusev
Application Deadline: 31 July 2020

Funding Type

PhD Type

Transparent conductors are essential for the sustainable future of advanced societies as they are key to the reduction of the energy demand in buildings, and also as transparent electrodes in solar energy conversion.
  Discovery of transparent conductors for sustainable buildings using Machine Learning and Advanced Data Analytics (Reference NSGML2020CS)
  Prof J Y Goulermas, Prof M J Rosseinsky, Dr M Gaultois, Dr V Gusev
Application Deadline: 31 July 2020

Funding Type

PhD Type

Transparent conductors are essential for the sustainable future of advanced societies as they are key to the reduction of the energy demand in buildings, and also as transparent electrodes in solar energy conversion.
  Towards Data Driven Aerodynamic Models: Data Fusion of Experiment and Simulation (EPSRC CDT in Distributed Algorithms)
  Dr S Timme, Dr J Castagna
Application Deadline: 31 May 2020

Funding Type

PhD Type

This studentship has been developed by the University of Liverpool in partnership with Aircraft Research Association Ltd.
  Extracting Important Information from Noisy Spectra (EPSRC CDT in Distributed Algorithms)
  Dr M Broccardo, Dr L Mason
Application Deadline: 31 May 2020

Funding Type

PhD Type

The University of Liverpool and STFC’s Hartree centre have developed this studentship in partnership with Gencoa, a medium-sized company based in Liverpool.
  Optimized Sampling Approaches for Compressive Sensing in Multi-Dimensional Datastreams (EPSRC CDT in Distributed Algorithms)
  Prof K Chen, Dr Y Zheng
Application Deadline: 31 May 2020

Funding Type

PhD Type

This studentship has been developed by the University of Liverpool in partnership with Sivananthan Laboratories, a small business specializing in state-of-the-art night vision, electron microscopy and hyperspectral imaging technologies.
  Learning to See More: Better Bayesian Track Before Detect using Statistical Machine Learning (EPSRC CDT in Distributed Algorithms)
  Dr A Garcia-Fernandez, Prof V Alexandrov
Application Deadline: 31 May 2020

Funding Type

PhD Type

This studentship has been developed by the University of Liverpool and STFC’s Hartree Centre in partnership with Leonardo. This project is concerned with improving the ability to detect faint objects in, for example, radar data.
  A rigorous identification of all metastable polymorphs for better and safer drugs
  Dr V Kurlin, Dr A Pulido
Application Deadline: 12 April 2020

Funding Type

PhD Type

The aim is to develop new geometric methods for quantifying a similarity between crystal structures to substantially speed-up the Crystal Structure Prediction in Materials Science.
  Machine learning approaches for the clinical diagnosis of autoimmune disease
  Dr A Auer-Fowler, Dr P Green
Application Deadline: 12 April 2020

Funding Type

PhD Type

Autoimmune diseases have a range of complex symptoms and are therefore typically hard to diagnose. Current diagnostic methods are often invasive, painful, and subjective.
  Dam reliability assessment
  Prof S Ferson, Dr L Di Sarno
Application Deadline: 30 April 2020

Funding Type

PhD Type

Dam failures can be estimated by structural reliabily analysis given local environmental conditions and history (if available). Such analysis can be used to identify dams at risk and derive fragility curves for multiple natural and man-made hazards.
  Moving the tipping point: the decision to anticoagulate patients with atrial fibrillation
  Dr RW Wilkinson, Dr IS Siekmann, Prof G Lip, Prof P Lisboa
Applications accepted all year round

Funding Type

PhD Type

Atrial fibrillation (AF) is a heart condition that causes an irregular and often abnormally fast heart rate, and leads to a high risk of stroke and death.
  Understanding and dissecting microbial communities with Raman spectroscopy
  Prof R Goodacre
Applications accepted all year round

Funding Type

PhD Type

We have shown that it is possible to label bacteria with heavy isotopes of carbon (13C) and nitrogen (15N) and this incorporation within cellular biomass of heavy isotopes has led to significant shifts in certain vibrational modes from amino acids and nucleic acids that can be readily detected with Raman spectroscopy.
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