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Statistics PhD Projects, Programs & Scholarships in Glasgow

We have 10 Statistics PhD Projects, Programs & Scholarships in Glasgow

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  Measuring symptoms of prolapse with the Pelvic Organ Prolapse Symptom Score (POP-SS)
  Research Group: NMAHP Research Unit
  Dr S Hagan, Dr P Dall, Dr P McCarthy
Applications accepted all year round

Funding Type

PhD Type

Ref. SHLS20038 Hagen. Background. Pelvic organ prolapse is a common female condition characterised by symptomatic descent of the vaginal walls, apex or vault from the normal anatomical position.
  PhD in Computing Science – Machine Learning in HealthCare Informatics
  Dr F Deligianni
Application Deadline: 30 June 2020

Funding Type

PhD Type

There is funding for a PhD in Data Science with relation to the analysis of neurophysiological/healthcare data. Current progress in machine learning has made massive steps towards intelligent systems that match or even surpass human abilities in applications such as computer vision and information retrieval.
  Variational Bayesian Inversion Approaches for Ultrasonic Tomography
  Dr K Tant, Dr M Foondun, Dr V Dolean Maini
Application Deadline: 28 June 2020

Funding Type

PhD Type

A fully-funded four year PhD studentship is available, starting on or after 1st October 2020.
  PhD in Engineering Optimization
  Dr B Liu, Dr O Onireti, Prof M Imran
Application Deadline: 31 July 2020

Funding Type

PhD Type

Applications are invited for a four-year fully funded PhD research studentship in engineering design optimization in the Communications, Sensing and Imaging (CSI) Group, James Watt School of Engineering, University of Glasgow, Scotland, UK.
  PhD in Engineering - Remote Imaging for Infectious Disease Diagnosis
  Prof J Cooper, Prof D Faccio, Dr J Reboud
Application Deadline: 1 June 2020

Funding Type

PhD Type

Professors Cooper and Faccio have a collaborative research programme on the use of quantum sensor technology and quantum-inspired imaging, combined with artificial intelligence (AI), to provide new sensors systems for future “intelligent homes”.
  Artificial Intelligence and Machine Learning to Deliver Enhanced Through-Life Nuclear Asset Management
  Prof S McArthur, Dr B Brown
Applications accepted all year round

Funding Type

PhD Type

The University of Strathclyde’s Advanced Nuclear Research Centre (ANRC) is offering a 3 year fully funded PhD in the field of industrial informatics, to investigate the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to Prognostics and Health Management (PHM) in the nuclear industry.
  PhD in AI and Engineering - AI Techniques for Automated Design of Microwave Antennas
  Dr B Liu, Prof M Imran, Dr M Ur Rehman
Application Deadline: 31 May 2020

Funding Type

PhD Type

Candidate students with AI, computer science or applied mathematics backgrounds are especially welcomed.
  Developing a multiscale description of complex fluids from the molecular to the continuum scales
  Dr L Lue, Dr D Kivotides
Applications accepted all year round

Funding Type

PhD Type

Engineers have traditionally used continuum equations to model processes, such as the Navier-Stokes equation for fluid flow. These methods require constitutive relations that encode the properties of the fluid; these are typically determined empirically and have limited predictive capabilities.
  Modelling the effect of the electric double layer on electron transfer kinetics
  Dr L Lue
Applications accepted all year round

Funding Type

PhD Type

Electron transfer between molecules in solution and a surface (e.g., a metallic electrode or semiconducting substrate) occurs in a wide variety of important areas, including catalysis, corrosion, electrodeposition, photochemistry, etc.
  2019 Strathclyde SEA FindAPhD advert: Measuring security of Industry 4.0 systems
  Dr D Thomas
Applications accepted all year round

Funding Type

PhD Type

Introduction. Industry 4.0 will lead to many more critical industrial systems being Internet-connected and interdependent. Measuring the security of these distributed systems of industrial devices is vital to understand the risk of compromise this poses and to assess potential defences.
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